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CHECK report for HIBAG on tokay1

This page was generated on 2019-04-13 11:24:53 -0400 (Sat, 13 Apr 2019).

Package 717/1649HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
HIBAG 1.18.1
Xiuwen Zheng
Snapshot Date: 2019-04-12 17:01:30 -0400 (Fri, 12 Apr 2019)
URL: https://git.bioconductor.org/packages/HIBAG
Branch: RELEASE_3_8
Last Commit: 5692a74
Last Changed Date: 2018-11-15 00:04:33 -0400 (Thu, 15 Nov 2018)
malbec1 Linux (Ubuntu 16.04.6 LTS) / x86_64  OK  OK  OK UNNEEDED, same version exists in internal repository
tokay1 Windows Server 2012 R2 Standard / x64  OK  OK [ OK ] OK UNNEEDED, same version exists in internal repository
merida1 OS X 10.11.6 El Capitan / x86_64  OK  OK  OK  OK UNNEEDED, same version exists in internal repository

Summary

Package: HIBAG
Version: 1.18.1
Command: C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:HIBAG.install-out.txt --library=C:\Users\biocbuild\bbs-3.8-bioc\R\library --no-vignettes --timings HIBAG_1.18.1.tar.gz
StartedAt: 2019-04-13 03:01:07 -0400 (Sat, 13 Apr 2019)
EndedAt: 2019-04-13 03:04:19 -0400 (Sat, 13 Apr 2019)
EllapsedTime: 192.3 seconds
RetCode: 0
Status:  OK  
CheckDir: HIBAG.Rcheck
Warnings: 0

Command output

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###
### Running command:
###
###   C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:HIBAG.install-out.txt --library=C:\Users\biocbuild\bbs-3.8-bioc\R\library --no-vignettes --timings HIBAG_1.18.1.tar.gz
###
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* using log directory 'C:/Users/biocbuild/bbs-3.8-bioc/meat/HIBAG.Rcheck'
* using R version 3.5.3 (2019-03-11)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'HIBAG/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'HIBAG' version '1.18.1'
* checking package namespace information ... OK
* checking package dependencies ...Warning: unable to access index for repository https://CRAN.R-project.org/src/contrib:
  cannot open URL 'https://CRAN.R-project.org/src/contrib/PACKAGES'
 OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'HIBAG' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking 'build' directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* loading checks for arch 'i386'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* loading checks for arch 'x64'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of 'data' directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking compiled code ... NOTE
Note: information on .o files for i386 is not available
Note: information on .o files for x64 is not available
File 'C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/libs/i386/HIBAG.dll':
  Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran)
  Found 'exit', possibly from 'exit' (C), 'stop' (Fortran)
  Found 'printf', possibly from 'printf' (C)

Compiled code should not call entry points which might terminate R nor
write to stdout/stderr instead of to the console, nor use Fortran I/O
nor system RNGs. The detected symbols are linked into the code but
might come from libraries and not actually be called.

See 'Writing portable packages' in the 'Writing R Extensions' manual.
* checking installed files from 'inst/doc' ... OK
* checking files in 'vignettes' ... OK
* checking examples ...
** running examples for arch 'i386' ... OK
** running examples for arch 'x64' ... OK
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
** running tests for arch 'i386' ...
  Running 'runTests.R'
 OK
** running tests for arch 'x64' ...
  Running 'runTests.R'
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in 'inst/doc' ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 1 NOTE
See
  'C:/Users/biocbuild/bbs-3.8-bioc/meat/HIBAG.Rcheck/00check.log'
for details.



Installation output

HIBAG.Rcheck/00install.out

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###
### Running command:
###
###   C:\cygwin\bin\curl.exe -O https://malbec1.bioconductor.org/BBS/3.8/bioc/src/contrib/HIBAG_1.18.1.tar.gz && rm -rf HIBAG.buildbin-libdir && mkdir HIBAG.buildbin-libdir && C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=HIBAG.buildbin-libdir HIBAG_1.18.1.tar.gz && C:\Users\biocbuild\bbs-3.8-bioc\R\bin\R.exe CMD INSTALL HIBAG_1.18.1.zip && rm HIBAG_1.18.1.tar.gz HIBAG_1.18.1.zip
###
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  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100  559k  100  559k    0     0  12.7M      0 --:--:-- --:--:-- --:--:-- 14.0M

install for i386

* installing *source* package 'HIBAG' ...
** libs
C:/Rtools/mingw_32/bin/g++  -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG     -I"C:/extsoft/include"     -O2 -Wall  -mtune=generic -c HIBAG.cpp -o HIBAG.o
C:/Rtools/mingw_32/bin/g++  -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG     -I"C:/extsoft/include"     -O2 -Wall  -mtune=generic -c LibHLA.cpp -o LibHLA.o
C:/Rtools/mingw_32/bin/g++ -shared -s -static-libgcc -o HIBAG.dll tmp.def HIBAG.o LibHLA.o -LC:/extsoft/lib/i386 -LC:/extsoft/lib -LC:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/bin/i386 -lR
installing to C:/Users/biocbuild/bbs-3.8-bioc/meat/HIBAG.buildbin-libdir/HIBAG/libs/i386
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
  converting help for package 'HIBAG'
    finding HTML links ... done
    HIBAG-package                           html  
    HLA_Type_Table                          html  
    HapMap_CEU_Geno                         html  
    hlaAASeqClass                           html  
    hlaAllele                               html  
    hlaAlleleClass                          html  
    hlaAlleleDigit                          html  
    hlaAlleleSubset                         html  
    hlaAssocTest                            html  
    hlaAttrBagClass                         html  
    hlaAttrBagObj                           html  
    hlaAttrBagging                          html  
    hlaBED2Geno                             html  
    hlaCheckAllele                          html  
    hlaCheckSNPs                            html  
    hlaClose                                html  
    hlaCombineAllele                        html  
    hlaCombineModelObj                      html  
    hlaCompareAllele                        html  
    hlaConvSequence                         html  
    hlaDistance                             html  
    hlaErrMsg                               html  
    hlaFlankingSNP                          html  
    hlaGDS2Geno                             html  
    hlaGeno2PED                             html  
    hlaGenoAFreq                            html  
    hlaGenoCombine                          html  
    hlaGenoLD                               html  
    hlaGenoMFreq                            html  
    hlaGenoMRate                            html  
    hlaGenoMRate_Samp                       html  
    hlaGenoSubset                           html  
    hlaGenoSwitchStrand                     html  
    hlaLDMatrix                             html  
    hlaLociInfo                             html  
    hlaMakeSNPGeno                          html  
    hlaModelFiles                           html  
    hlaModelFromObj                         html  
    hlaOutOfBag                             html  
    hlaParallelAttrBagging                  html  
    hlaPredMerge                            html  
    hlaPublish                              html  
    hlaReport                               html  
    hlaReportPlot                           html  
    hlaSNPGenoClass                         html  
    hlaSNPID                                html  
    hlaSampleAllele                         html  
    hlaSplitAllele                          html  
    hlaSubModelObj                          html  
    hlaUniqueAllele                         html  
    plot.hlaAttrBagObj                      html  
    predict.hlaAttrBagClass                 html  
    print.hlaAttrBagClass                   html  
    summary.hlaAlleleClass                  html  
    summary.hlaSNPGenoClass                 html  
** building package indices
** installing vignettes
   'HIBAG_Tutorial.Rmd' 
** testing if installed package can be loaded
In R CMD INSTALL

install for x64

* installing *source* package 'HIBAG' ...
** libs
C:/Rtools/mingw_64/bin/g++  -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG     -I"C:/extsoft/include"     -O2 -Wall  -mtune=generic -c HIBAG.cpp -o HIBAG.o
C:/Rtools/mingw_64/bin/g++  -I"C:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/include" -DNDEBUG     -I"C:/extsoft/include"     -O2 -Wall  -mtune=generic -c LibHLA.cpp -o LibHLA.o
C:/Rtools/mingw_64/bin/g++ -shared -s -static-libgcc -o HIBAG.dll tmp.def HIBAG.o LibHLA.o -LC:/extsoft/lib/x64 -LC:/extsoft/lib -LC:/Users/BIOCBU˜1/BBS-3˜1.8-B/R/bin/x64 -lR
installing to C:/Users/biocbuild/bbs-3.8-bioc/meat/HIBAG.buildbin-libdir/HIBAG/libs/x64
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'HIBAG' as HIBAG_1.18.1.zip
* DONE (HIBAG)
In R CMD INSTALL
In R CMD INSTALL
* installing to library 'C:/Users/biocbuild/bbs-3.8-bioc/R/library'
package 'HIBAG' successfully unpacked and MD5 sums checked
In R CMD INSTALL

Tests output

HIBAG.Rcheck/tests_i386/runTests.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> #############################################################
> #
> # DESCRIPTION: Unit tests in the HIBAG package
> #
> 
> # load the HIBAG package
> library(HIBAG)
HIBAG (HLA Genotype Imputation with Attribute Bagging)
Kernel Version: v1.4
> 
> 
> #############################################################
> 
> # a list of HLA genes
> hla.list <- c("A", "B", "C", "DQA1", "DQB1", "DRB1")
> 
> # pre-defined lower bound of prediction accuracy
> hla.acc <- c(0.9, 0.8, 0.8, 0.85, 0.85, 0.75)
> 
> 
> for (hla.idx in seq_len(length(hla.list)))
+ {
+ 	hla.id <- hla.list[hla.idx]
+ 
+ 	# make a "hlaAlleleClass" object
+ 	hla <- hlaAllele(HLA_Type_Table$sample.id,
+ 		H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")],
+ 		H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")],
+ 		locus=hla.id, assembly="hg19")
+ 
+ 	# divide HLA types randomly
+ 	set.seed(100)
+ 	hlatab <- hlaSplitAllele(hla, train.prop=0.5)
+ 
+ 	# SNP predictors within the flanking region on each side
+ 	region <- 500	# kb
+ 	snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id,
+ 		HapMap_CEU_Geno$snp.position,
+ 		hla.id, region*1000, assembly="hg19")
+ 
+ 	# training and validation genotypes
+ 	train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
+ 		snp.sel=match(snpid, HapMap_CEU_Geno$snp.id),
+ 		samp.sel=match(hlatab$training$value$sample.id,
+ 		HapMap_CEU_Geno$sample.id))
+ 	test.geno <- hlaGenoSubset(HapMap_CEU_Geno,
+ 		samp.sel=match(hlatab$validation$value$sample.id,
+ 		HapMap_CEU_Geno$sample.id))
+ 
+ 
+ 	# train a HIBAG model
+ 	set.seed(100)
+ 	model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=10)
+ 	summary(model)
+ 
+ 	# validation
+ 	pred <- predict(model, test.geno)
+ 	summary(pred)
+ 
+ 	# compare
+ 	comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
+ 		call.threshold=0)
+ 	print(comp$overall)
+ 
+ 	# check
+ 	if (comp$overall$acc.haplo < hla.acc[hla.idx])
+ 	{
+ 		stop("HLA - ", hla.id, ", 'acc.haplo' should be >= ",
+ 			hla.acc[hla.idx], ".")
+ 	}
+ 
+ 	cat("\n\n")
+ }
Exclude 9 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:17
[1] 2019-04-13 03:03:18, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
[2] 2019-04-13 03:03:18, OOB Acc: 92.31%, # of SNPs: 19, # of Haplo: 38
[3] 2019-04-13 03:03:18, OOB Acc: 86.36%, # of SNPs: 12, # of Haplo: 21
[4] 2019-04-13 03:03:18, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 44
[5] 2019-04-13 03:03:18, OOB Acc: 100.00%, # of SNPs: 14, # of Haplo: 30
[6] 2019-04-13 03:03:18, OOB Acc: 85.71%, # of SNPs: 18, # of Haplo: 47
[7] 2019-04-13 03:03:18, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 32
[8] 2019-04-13 03:03:19, OOB Acc: 83.33%, # of SNPs: 17, # of Haplo: 56
[9] 2019-04-13 03:03:19, OOB Acc: 100.00%, # of SNPs: 18, # of Haplo: 44
[10] 2019-04-13 03:03:19, OOB Acc: 81.82%, # of SNPs: 14, # of Haplo: 31
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002811482 0.0003054155 0.0005238215 0.0037455050 0.0101502970 0.0181041748 
        Max.         Mean           SD 
0.3808460815 0.0367397147 0.0874239077 
Accuracy with training data: 98.5%
Out-of-bag accuracy: 89.9%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 10
    total # of SNPs used: 92
    avg. # of SNPs in an individual classifier: 15.60
        (sd: 2.27, min: 12, max: 19, median: 15.00)
    avg. # of haplotypes in an individual classifier: 38.50
        (sd: 10.16, min: 21, max: 56, median: 40.00)
    avg. out-of-bag accuracy: 89.89%
        (sd: 6.29%, min: 81.82%, max: 100.00%, median: 89.20%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002811482 0.0003054155 0.0005238215 0.0037455050 0.0101502970 0.0181041748 
        Max.         Mean           SD 
0.3808460815 0.0367397147 0.0874239077 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:19)	0%
Predicting (2019-04-13 03:03:19)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.8%)  4 (15.4%)  4 (15.4%) 17 (65.4%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0008583 0.0066451 0.0258049 0.0258304 0.3808461 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            26          25            51 0.9615385 0.9807692              0
  n.call call.rate
1     26         1


Exclude 1 monomorphic SNP
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 340, # of samples: 28
# of unique HLA alleles: 22
[-] 2019-04-13 03:03:19
[1] 2019-04-13 03:03:20, OOB Acc: 75.00%, # of SNPs: 18, # of Haplo: 127
[2] 2019-04-13 03:03:21, OOB Acc: 72.73%, # of SNPs: 20, # of Haplo: 118
[3] 2019-04-13 03:03:21, OOB Acc: 60.00%, # of SNPs: 16, # of Haplo: 27
[4] 2019-04-13 03:03:21, OOB Acc: 70.83%, # of SNPs: 16, # of Haplo: 94
[5] 2019-04-13 03:03:21, OOB Acc: 63.64%, # of SNPs: 11, # of Haplo: 39
[6] 2019-04-13 03:03:21, OOB Acc: 81.25%, # of SNPs: 18, # of Haplo: 97
[7] 2019-04-13 03:03:21, OOB Acc: 55.56%, # of SNPs: 13, # of Haplo: 52
[8] 2019-04-13 03:03:22, OOB Acc: 83.33%, # of SNPs: 20, # of Haplo: 84
[9] 2019-04-13 03:03:22, OOB Acc: 45.45%, # of SNPs: 10, # of Haplo: 16
[10] 2019-04-13 03:03:22, OOB Acc: 73.08%, # of SNPs: 15, # of Haplo: 71
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0001475023 0.0001478264 0.0001507439 0.0027105247 0.0048336862 0.0125263769 
        Max.         Mean           SD 
0.0738641615 0.0109592146 0.0155234908 
Accuracy with training data: 94.6%
Out-of-bag accuracy: 68.1%
Gene: B
Training dataset: 28 samples X 340 SNPs
    # of HLA alleles: 22
    # of individual classifiers: 10
    total # of SNPs used: 98
    avg. # of SNPs in an individual classifier: 15.70
        (sd: 3.50, min: 10, max: 20, median: 16.00)
    avg. # of haplotypes in an individual classifier: 72.50
        (sd: 38.07, min: 16, max: 127, median: 77.50)
    avg. out-of-bag accuracy: 68.09%
        (sd: 11.84%, min: 45.45%, max: 83.33%, median: 71.78%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0001475023 0.0001478264 0.0001507439 0.0027105247 0.0048336862 0.0125263769 
        Max.         Mean           SD 
0.0738641615 0.0109592146 0.0155234908 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 340 SNPs, 22 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 15
Predicting (2019-04-13 03:03:22)	0%
Predicting (2019-04-13 03:03:22)	100%
Gene: B
Range: [31321649bp, 31324989bp] on hg19
# of samples: 15
# of unique HLA alleles: 9
# of unique HLA genotypes: 13
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
 2 (13.3%)  6 (40.0%)  3 (20.0%)  4 (26.7%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
1.000e-08 5.102e-04 2.615e-03 1.161e-02 9.485e-03 8.519e-02 
  total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1            15          12            27     0.8       0.9              0
  n.call call.rate
1     15         1


Exclude 2 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 354, # of samples: 36
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:22
[1] 2019-04-13 03:03:22, OOB Acc: 96.15%, # of SNPs: 19, # of Haplo: 35
[2] 2019-04-13 03:03:23, OOB Acc: 90.91%, # of SNPs: 25, # of Haplo: 75
[3] 2019-04-13 03:03:23, OOB Acc: 66.67%, # of SNPs: 12, # of Haplo: 42
[4] 2019-04-13 03:03:23, OOB Acc: 96.43%, # of SNPs: 22, # of Haplo: 32
[5] 2019-04-13 03:03:23, OOB Acc: 83.33%, # of SNPs: 18, # of Haplo: 42
[6] 2019-04-13 03:03:23, OOB Acc: 76.67%, # of SNPs: 17, # of Haplo: 40
[7] 2019-04-13 03:03:23, OOB Acc: 88.46%, # of SNPs: 20, # of Haplo: 44
[8] 2019-04-13 03:03:23, OOB Acc: 67.86%, # of SNPs: 17, # of Haplo: 25
[9] 2019-04-13 03:03:24, OOB Acc: 86.36%, # of SNPs: 19, # of Haplo: 50
[10] 2019-04-13 03:03:24, OOB Acc: 100.00%, # of SNPs: 21, # of Haplo: 75
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0006082516 0.0006183124 0.0007088597 0.0014283040 0.0034803366 0.0089095913 
        Max.         Mean           SD 
0.0574239572 0.0093779653 0.0130718885 
Accuracy with training data: 100.0%
Out-of-bag accuracy: 85.3%
Gene: C
Training dataset: 36 samples X 354 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 10
    total # of SNPs used: 114
    avg. # of SNPs in an individual classifier: 19.00
        (sd: 3.46, min: 12, max: 25, median: 19.00)
    avg. # of haplotypes in an individual classifier: 46.00
        (sd: 16.76, min: 25, max: 75, median: 42.00)
    avg. out-of-bag accuracy: 85.28%
        (sd: 11.69%, min: 66.67%, max: 100.00%, median: 87.41%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0006082516 0.0006183124 0.0007088597 0.0014283040 0.0034803366 0.0089095913 
        Max.         Mean           SD 
0.0574239572 0.0093779653 0.0130718885 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 354 SNPs, 17 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 24
Predicting (2019-04-13 03:03:24)	0%
Predicting (2019-04-13 03:03:24)	100%
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 24
# of unique HLA alleles: 14
# of unique HLA genotypes: 16
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
 3 (12.5%)  4 (16.7%)  7 (29.2%) 10 (41.7%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0001148 0.0005418 0.0056976 0.0069050 0.0317705 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            24          17            41 0.7083333 0.8541667              0
  n.call call.rate
1     24         1


Exclude 6 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 343, # of samples: 31
# of unique HLA alleles: 7
[-] 2019-04-13 03:03:24
[1] 2019-04-13 03:03:24, OOB Acc: 86.36%, # of SNPs: 10, # of Haplo: 19
[2] 2019-04-13 03:03:24, OOB Acc: 92.31%, # of SNPs: 12, # of Haplo: 37
[3] 2019-04-13 03:03:24, OOB Acc: 86.36%, # of SNPs: 12, # of Haplo: 21
[4] 2019-04-13 03:03:24, OOB Acc: 95.00%, # of SNPs: 10, # of Haplo: 17
[5] 2019-04-13 03:03:24, OOB Acc: 88.89%, # of SNPs: 11, # of Haplo: 31
[6] 2019-04-13 03:03:24, OOB Acc: 100.00%, # of SNPs: 14, # of Haplo: 39
[7] 2019-04-13 03:03:24, OOB Acc: 90.00%, # of SNPs: 9, # of Haplo: 16
[8] 2019-04-13 03:03:24, OOB Acc: 95.45%, # of SNPs: 10, # of Haplo: 10
[9] 2019-04-13 03:03:24, OOB Acc: 83.33%, # of SNPs: 7, # of Haplo: 9
[10] 2019-04-13 03:03:24, OOB Acc: 87.50%, # of SNPs: 7, # of Haplo: 17
Calculating matching proportion:
       Min.    0.1% Qu.      1% Qu.     1st Qu.      Median     3rd Qu. 
0.002003247 0.002016202 0.002132800 0.009847689 0.018023446 0.040449819 
       Max.        Mean          SD 
0.477353285 0.059978120 0.120992882 
Accuracy with training data: 95.2%
Out-of-bag accuracy: 90.5%
Gene: DQA1
Training dataset: 31 samples X 343 SNPs
    # of HLA alleles: 7
    # of individual classifiers: 10
    total # of SNPs used: 72
    avg. # of SNPs in an individual classifier: 10.20
        (sd: 2.20, min: 7, max: 14, median: 10.00)
    avg. # of haplotypes in an individual classifier: 21.60
        (sd: 10.55, min: 9, max: 39, median: 18.00)
    avg. out-of-bag accuracy: 90.52%
        (sd: 5.12%, min: 83.33%, max: 100.00%, median: 89.44%)
Matching proportion:
       Min.    0.1% Qu.      1% Qu.     1st Qu.      Median     3rd Qu. 
0.002003247 0.002016202 0.002132800 0.009847689 0.018023446 0.040449819 
       Max.        Mean          SD 
0.477353285 0.059978120 0.120992882 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 343 SNPs, 7 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 29
Predicting (2019-04-13 03:03:24)	0%
Predicting (2019-04-13 03:03:24)	100%
Gene: DQA1
Range: [32605169bp, 32612152bp] on hg19
# of samples: 29
# of unique HLA alleles: 7
# of unique HLA genotypes: 16
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.4%)  7 (24.1%)  5 (17.2%) 16 (55.2%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0007125 0.0034788 0.0080649 0.0285052 0.0159776 0.4784200 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            29          23            51 0.7931034 0.8793103              0
  n.call call.rate
1     29         1


Exclude 5 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 351, # of samples: 34
# of unique HLA alleles: 12
[-] 2019-04-13 03:03:24
[1] 2019-04-13 03:03:24, OOB Acc: 81.82%, # of SNPs: 17, # of Haplo: 58
[2] 2019-04-13 03:03:25, OOB Acc: 76.92%, # of SNPs: 13, # of Haplo: 45
[3] 2019-04-13 03:03:25, OOB Acc: 86.67%, # of SNPs: 15, # of Haplo: 56
[4] 2019-04-13 03:03:25, OOB Acc: 78.57%, # of SNPs: 14, # of Haplo: 16
[5] 2019-04-13 03:03:25, OOB Acc: 89.29%, # of SNPs: 17, # of Haplo: 35
[6] 2019-04-13 03:03:25, OOB Acc: 86.36%, # of SNPs: 11, # of Haplo: 49
[7] 2019-04-13 03:03:25, OOB Acc: 79.17%, # of SNPs: 13, # of Haplo: 33
[8] 2019-04-13 03:03:25, OOB Acc: 92.31%, # of SNPs: 16, # of Haplo: 35
[9] 2019-04-13 03:03:25, OOB Acc: 91.67%, # of SNPs: 22, # of Haplo: 81
[10] 2019-04-13 03:03:26, OOB Acc: 87.50%, # of SNPs: 21, # of Haplo: 45
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0003081898 0.0003364790 0.0005910815 0.0036371930 0.0059946892 0.0156499571 
        Max.         Mean           SD 
0.4243764595 0.0464079969 0.1113675480 
Accuracy with training data: 94.1%
Out-of-bag accuracy: 85.0%
Gene: DQB1
Training dataset: 34 samples X 351 SNPs
    # of HLA alleles: 12
    # of individual classifiers: 10
    total # of SNPs used: 98
    avg. # of SNPs in an individual classifier: 15.90
        (sd: 3.51, min: 11, max: 22, median: 15.50)
    avg. # of haplotypes in an individual classifier: 45.30
        (sd: 17.59, min: 16, max: 81, median: 45.00)
    avg. out-of-bag accuracy: 85.03%
        (sd: 5.56%, min: 76.92%, max: 92.31%, median: 86.52%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0003081898 0.0003364790 0.0005910815 0.0036371930 0.0059946892 0.0156499571 
        Max.         Mean           SD 
0.4243764595 0.0464079969 0.1113675480 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 351 SNPs, 12 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:26)	0%
Predicting (2019-04-13 03:03:26)	100%
Gene: DQB1
Range: [32627241bp, 32634466bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 18
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  2 (7.7%)  6 (23.1%)  4 (15.4%) 14 (53.8%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
8.000e-09 3.339e-04 2.401e-03 3.725e-03 4.974e-03 1.488e-02 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            26          23            48 0.8846154 0.9230769              0
  n.call call.rate
1     26         1


Exclude 4 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 18
# of SNPs: 323, # of samples: 35
# of unique HLA alleles: 20
[-] 2019-04-13 03:03:26
[1] 2019-04-13 03:03:26, OOB Acc: 70.00%, # of SNPs: 19, # of Haplo: 83
[2] 2019-04-13 03:03:26, OOB Acc: 81.25%, # of SNPs: 18, # of Haplo: 102
[3] 2019-04-13 03:03:27, OOB Acc: 76.67%, # of SNPs: 20, # of Haplo: 138
[4] 2019-04-13 03:03:27, OOB Acc: 95.00%, # of SNPs: 16, # of Haplo: 65
[5] 2019-04-13 03:03:27, OOB Acc: 60.00%, # of SNPs: 16, # of Haplo: 78
[6] 2019-04-13 03:03:28, OOB Acc: 60.00%, # of SNPs: 15, # of Haplo: 63
[7] 2019-04-13 03:03:28, OOB Acc: 77.27%, # of SNPs: 19, # of Haplo: 71
[8] 2019-04-13 03:03:28, OOB Acc: 61.54%, # of SNPs: 12, # of Haplo: 27
[9] 2019-04-13 03:03:28, OOB Acc: 75.00%, # of SNPs: 15, # of Haplo: 54
[10] 2019-04-13 03:03:29, OOB Acc: 66.67%, # of SNPs: 20, # of Haplo: 60
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0003877617 0.0003912266 0.0004224100 0.0014197688 0.0026546695 0.0060312073 
        Max.         Mean           SD 
0.4385472809 0.0275068316 0.0991274560 
Accuracy with training data: 94.3%
Out-of-bag accuracy: 72.3%
Gene: DRB1
Training dataset: 35 samples X 323 SNPs
    # of HLA alleles: 20
    # of individual classifiers: 10
    total # of SNPs used: 118
    avg. # of SNPs in an individual classifier: 17.00
        (sd: 2.62, min: 12, max: 20, median: 17.00)
    avg. # of haplotypes in an individual classifier: 74.10
        (sd: 29.80, min: 27, max: 138, median: 68.00)
    avg. out-of-bag accuracy: 72.34%
        (sd: 11.06%, min: 60.00%, max: 95.00%, median: 72.50%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0003877617 0.0003912266 0.0004224100 0.0014197688 0.0026546695 0.0060312073 
        Max.         Mean           SD 
0.4385472809 0.0275068316 0.0991274560 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 323 SNPs, 20 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 25
Predicting (2019-04-13 03:03:29)	0%
Predicting (2019-04-13 03:03:29)	100%
Gene: DRB1
Range: [32546546bp, 32557613bp] on hg19
# of samples: 25
# of unique HLA alleles: 9
# of unique HLA genotypes: 16
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
 4 (16.0%)  8 (32.0%)  7 (28.0%)  6 (24.0%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0000408 0.0005342 0.0269892 0.0015067 0.4388277 
  total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1            25          15            38     0.6      0.76              0
  n.call call.rate
1     25         1


> 
> 
> 
> #############################################################
> 
> {
+ 	function.list <- readRDS(
+ 		system.file("Meta", "Rd.rds", package="HIBAG"))$Name
+ 
+ 	sapply(function.list, FUN = function(func.name)
+ 		{
+ 			args <- list(
+ 				topic   = func.name,
+ 				package = "HIBAG",
+ 				echo = FALSE,
+ 				verbose = FALSE,
+ 				ask = FALSE
+ 			)
+ 			suppressWarnings(do.call(example, args))
+ 			NULL
+ 		})
+ 	invisible()
+ }
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:29
     1, SNP: 155, Loss: 186.198, OOB Acc: 68.18%, # of Haplo: 13
     2, SNP: 111, Loss: 138.902, OOB Acc: 72.73%, # of Haplo: 13
     3, SNP: 159, Loss: 134.624, OOB Acc: 81.82%, # of Haplo: 15
     4, SNP: 75, Loss: 120.53, OOB Acc: 86.36%, # of Haplo: 16
     5, SNP: 129, Loss: 79.3436, OOB Acc: 90.91%, # of Haplo: 20
     6, SNP: 135, Loss: 56.5856, OOB Acc: 90.91%, # of Haplo: 20
     7, SNP: 162, Loss: 40.1347, OOB Acc: 90.91%, # of Haplo: 20
     8, SNP: 192, Loss: 29.7565, OOB Acc: 90.91%, # of Haplo: 24
     9, SNP: 182, Loss: 23.1586, OOB Acc: 90.91%, # of Haplo: 24
    10, SNP: 24, Loss: 16.7007, OOB Acc: 90.91%, # of Haplo: 27
    11, SNP: 154, Loss: 15.6368, OOB Acc: 90.91%, # of Haplo: 28
    12, SNP: 17, Loss: 15.4059, OOB Acc: 90.91%, # of Haplo: 39
    13, SNP: 199, Loss: 14.4894, OOB Acc: 90.91%, # of Haplo: 40
    14, SNP: 73, Loss: 8.03154, OOB Acc: 90.91%, # of Haplo: 42
    15, SNP: 151, Loss: 5.75619, OOB Acc: 90.91%, # of Haplo: 42
[1] 2019-04-13 03:03:29, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
     1, SNP: 87, Loss: 163.788, OOB Acc: 50.00%, # of Haplo: 15
     2, SNP: 179, Loss: 159.036, OOB Acc: 57.69%, # of Haplo: 16
     3, SNP: 162, Loss: 127.744, OOB Acc: 61.54%, # of Haplo: 17
     4, SNP: 113, Loss: 120.348, OOB Acc: 76.92%, # of Haplo: 17
     5, SNP: 149, Loss: 99.7027, OOB Acc: 80.77%, # of Haplo: 19
     6, SNP: 183, Loss: 63.9201, OOB Acc: 80.77%, # of Haplo: 19
     7, SNP: 82, Loss: 46.9454, OOB Acc: 80.77%, # of Haplo: 19
     8, SNP: 73, Loss: 36.2707, OOB Acc: 84.62%, # of Haplo: 21
     9, SNP: 47, Loss: 34.9876, OOB Acc: 84.62%, # of Haplo: 22
    10, SNP: 140, Loss: 27.9494, OOB Acc: 84.62%, # of Haplo: 22
    11, SNP: 189, Loss: 23.9141, OOB Acc: 84.62%, # of Haplo: 23
    12, SNP: 249, Loss: 18.884, OOB Acc: 84.62%, # of Haplo: 27
    13, SNP: 151, Loss: 15.8511, OOB Acc: 84.62%, # of Haplo: 27
    14, SNP: 21, Loss: 11.1982, OOB Acc: 88.46%, # of Haplo: 30
    15, SNP: 215, Loss: 10.7762, OOB Acc: 88.46%, # of Haplo: 32
    16, SNP: 136, Loss: 10.5472, OOB Acc: 88.46%, # of Haplo: 38
    17, SNP: 117, Loss: 10.3045, OOB Acc: 88.46%, # of Haplo: 38
    18, SNP: 106, Loss: 10.3041, OOB Acc: 92.31%, # of Haplo: 38
    19, SNP: 199, Loss: 9.5325, OOB Acc: 92.31%, # of Haplo: 38
[2] 2019-04-13 03:03:29, OOB Acc: 92.31%, # of SNPs: 19, # of Haplo: 38
     1, SNP: 135, Loss: 156.001, OOB Acc: 50.00%, # of Haplo: 13
     2, SNP: 141, Loss: 121.763, OOB Acc: 59.09%, # of Haplo: 14
     3, SNP: 162, Loss: 110.134, OOB Acc: 63.64%, # of Haplo: 14
     4, SNP: 73, Loss: 95.625, OOB Acc: 72.73%, # of Haplo: 15
     5, SNP: 96, Loss: 87.0485, OOB Acc: 86.36%, # of Haplo: 16
     6, SNP: 129, Loss: 49.5862, OOB Acc: 86.36%, # of Haplo: 18
     7, SNP: 147, Loss: 27.5253, OOB Acc: 86.36%, # of Haplo: 19
     8, SNP: 105, Loss: 16.9538, OOB Acc: 86.36%, # of Haplo: 19
     9, SNP: 258, Loss: 11.9891, OOB Acc: 86.36%, # of Haplo: 19
    10, SNP: 160, Loss: 10.5954, OOB Acc: 86.36%, # of Haplo: 20
    11, SNP: 199, Loss: 9.81573, OOB Acc: 86.36%, # of Haplo: 20
    12, SNP: 151, Loss: 9.3333, OOB Acc: 86.36%, # of Haplo: 21
[3] 2019-04-13 03:03:29, OOB Acc: 86.36%, # of SNPs: 12, # of Haplo: 21
     1, SNP: 184, Loss: 221.947, OOB Acc: 63.64%, # of Haplo: 15
     2, SNP: 87, Loss: 159.682, OOB Acc: 77.27%, # of Haplo: 18
     3, SNP: 84, Loss: 135.191, OOB Acc: 81.82%, # of Haplo: 21
     4, SNP: 36, Loss: 128.841, OOB Acc: 86.36%, # of Haplo: 23
     5, SNP: 109, Loss: 94.3071, OOB Acc: 90.91%, # of Haplo: 27
     6, SNP: 130, Loss: 66.1551, OOB Acc: 90.91%, # of Haplo: 29
     7, SNP: 64, Loss: 50.915, OOB Acc: 90.91%, # of Haplo: 29
     8, SNP: 194, Loss: 41.1566, OOB Acc: 90.91%, # of Haplo: 29
     9, SNP: 193, Loss: 32.6689, OOB Acc: 90.91%, # of Haplo: 30
    10, SNP: 123, Loss: 30.6255, OOB Acc: 90.91%, # of Haplo: 30
    11, SNP: 55, Loss: 30.1894, OOB Acc: 90.91%, # of Haplo: 35
    12, SNP: 95, Loss: 29.8967, OOB Acc: 90.91%, # of Haplo: 43
    13, SNP: 151, Loss: 25.6379, OOB Acc: 90.91%, # of Haplo: 43
    14, SNP: 199, Loss: 24.4761, OOB Acc: 90.91%, # of Haplo: 44
[4] 2019-04-13 03:03:29, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 44
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002704645 0.0002740287 0.0003061065 0.0041563208 0.0120425968 0.0259547286 
        Max.         Mean           SD 
0.3993959563 0.0433199485 0.0939557543 
Accuracy with training data: 98.5%
Out-of-bag accuracy: 90.1%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 47
    avg. # of SNPs in an individual classifier: 15.00
        (sd: 2.94, min: 12, max: 19, median: 14.50)
    avg. # of haplotypes in an individual classifier: 36.25
        (sd: 10.47, min: 21, max: 44, median: 40.00)
    avg. out-of-bag accuracy: 90.12%
        (sd: 2.59%, min: 86.36%, max: 92.31%, median: 90.91%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002704645 0.0002740287 0.0003061065 0.0041563208 0.0120425968 0.0259547286 
        Max.         Mean           SD 
0.3993959563 0.0433199485 0.0939557543 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:29)	0%
Predicting (2019-04-13 03:03:29)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.8%)   1 (3.8%)  8 (30.8%) 16 (61.5%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0009577 0.0048947 0.0311502 0.0380943 0.3993960 
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:29)	0%
Predicting (2019-04-13 03:03:29)	100%
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 90
Predicting (2019-04-13 03:03:29)	0%
Predicting (2019-04-13 03:03:29)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
using the default genome assembly (assembly="hg19")
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 12
# of unique HLA genotypes: 28
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 100
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
dominant model:
      [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p
24:02    49        11    42.9        81.8   4.0074  0.045*   0.042*
-----                                                              
01:01    36        24    50.0        50.0   0.0000  1.000    1.000 
02:01    25        35    52.0        48.6   0.0000  1.000    1.000 
02:06    59         1    50.8         0.0   0.0000  1.000    1.000 
03:01    51         9    49.0        55.6   0.0000  1.000    1.000 
11:01    55         5    50.9        40.0   0.0000  1.000    1.000 
23:01    58         2    50.0        50.0   0.0000  1.000    1.000 
24:03    59         1    50.8         0.0   0.0000  1.000    1.000 
25:01    55         5    52.7        20.0   0.8727  0.350    0.353 
26:01    57         3    52.6         0.0   1.4035  0.236    0.237 
29:02    56         4    51.8        25.0   0.2679  0.605    0.612 
31:01    57         3    49.1        66.7   0.0000  1.000    1.000 
32:01    56         4    46.4       100.0   2.4107  0.121    0.112 
68:01    57         3    52.6         0.0   1.4035  0.236    0.237 
additive model:
      [-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p
01:01  95  25  50.5  48.0   0.0000  1.000    1.000 
02:01  77  43  48.1  53.5   0.1450  0.703    0.704 
02:06 119   1  50.4   0.0   0.0000  1.000    1.000 
03:01 111   9  49.5  55.6   0.0000  1.000    1.000 
11:01 115   5  50.4  40.0   0.0000  1.000    1.000 
23:01 117   3  50.4  33.3   0.0000  1.000    1.000 
24:02 109  11  46.8  81.8   3.6030  0.058    0.053 
24:03 119   1  50.4   0.0   0.0000  1.000    1.000 
25:01 115   5  51.3  20.0   0.8348  0.361    0.364 
26:01 117   3  51.3   0.0   1.3675  0.242    0.244 
29:02 116   4  50.9  25.0   0.2586  0.611    0.619 
31:01 117   3  49.6  66.7   0.0000  1.000    1.000 
32:01 116   4  48.3 100.0   2.3276  0.127    0.119 
68:01 117   3  51.3   0.0   1.3675  0.242    0.244 
recessive model:
      [-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p
01:01        59     1        50.8       0    0.000  1.000    1.000 
02:01        52     8        46.2      75    1.298  0.255    0.254 
02:06        60     0        50.0       .        .       .        .
03:01        60     0        50.0       .        .       .        .
11:01        60     0        50.0       .        .       .        .
23:01        59     1        50.8       0    0.000  1.000    1.000 
24:02        60     0        50.0       .        .       .        .
24:03        60     0        50.0       .        .       .        .
25:01        60     0        50.0       .        .       .        .
26:01        60     0        50.0       .        .       .        .
29:02        60     0        50.0       .        .       .        .
31:01        60     0        50.0       .        .       .        .
32:01        60     0        50.0       .        .       .        .
68:01        60     0        50.0       .        .       .        .
genotype model:
      [-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02    49    11     0    42.9    81.8       .   4.0074  0.045*   0.042*
-----                                                                    
01:01    36    23     1    50.0    52.2       0   1.0435  0.593    1.000 
02:01    25    27     8    52.0    40.7      75   2.9659  0.227    0.271 
02:06    59     1     0    50.8     0.0       .   0.0000  1.000    1.000 
03:01    51     9     0    49.0    55.6       .   0.0000  1.000    1.000 
11:01    55     5     0    50.9    40.0       .   0.0000  1.000    1.000 
23:01    58     1     1    50.0   100.0       0   2.0000  0.368    1.000 
24:03    59     1     0    50.8     0.0       .   0.0000  1.000    1.000 
25:01    55     5     0    52.7    20.0       .   0.8727  0.350    0.353 
26:01    57     3     0    52.6     0.0       .   1.4035  0.236    0.237 
29:02    56     4     0    51.8    25.0       .   0.2679  0.605    0.612 
31:01    57     3     0    49.1    66.7       .   0.0000  1.000    1.000 
32:01    56     4     0    46.4   100.0       .   2.4107  0.121    0.112 
68:01    57     3     0    52.6     0.0       .   1.4035  0.236    0.237 
dominant model:
      [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p
01:01    36        24  -0.14684     -0.117427  0.909 
02:01    25        35  -0.32331     -0.000618  0.190 
02:06    59         1  -0.14024      0.170057       .
03:01    51         9  -0.05600     -0.583178  0.147 
11:01    55         5  -0.19188      0.489815  0.287 
23:01    58         2  -0.15400      0.413687  0.281 
24:02    49        11  -0.10486     -0.269664  0.537 
24:03    59         1  -0.11409     -1.373118       .
25:01    55         5  -0.12237     -0.274749  0.742 
26:01    57         3  -0.12473     -0.331558  0.690 
29:02    56         4  -0.13044     -0.199941  0.789 
31:01    57         3  -0.10097     -0.783003  0.607 
32:01    56         4  -0.07702     -0.947791  0.092 
68:01    57         3  -0.16915      0.512457  0.196 
genotype model:
      [-/-] [-/h] [h/h] avg.[-/-] avg.[-/h] avg.[h/h] anova.p
01:01    36    23     1  -0.14684  -0.08833  -0.78655  0.784 
02:01    25    27     8  -0.32331  -0.02341   0.07631  0.446 
02:06    59     1     0  -0.14024   0.17006         .  0.756 
03:01    51     9     0  -0.05600  -0.58318         .  0.138 
11:01    55     5     0  -0.19188   0.48981         .  0.137 
23:01    58     1     1  -0.15400   0.10762   0.71975  0.663 
24:02    49    11     0  -0.10486  -0.26966         .  0.618 
24:03    59     1     0  -0.11409  -1.37312         .  0.205 
25:01    55     5     0  -0.12237  -0.27475         .  0.742 
26:01    57     3     0  -0.12473  -0.33156         .  0.725 
29:02    56     4     0  -0.13044  -0.19994         .  0.892 
31:01    57     3     0  -0.10097  -0.78300         .  0.243 
32:01    56     4     0  -0.07702  -0.94779         .  0.086 
68:01    57     3     0  -0.16915   0.51246         .  0.243 
Logistic regression (dominant model) with 60 individuals:
  glm(case ˜ h, family = binomial, data = data)
      [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p      h.est
24:02    49        11    42.9        81.8   4.0074  0.045*   0.042*  1.792e+00
-----                                                                         
01:01    36        24    50.0        50.0   0.0000  1.000    1.000   4.657e-17
02:01    25        35    52.0        48.6   0.0000  1.000    1.000  -1.372e-01
02:06    59         1    50.8         0.0   0.0000  1.000    1.000  -1.560e+01
03:01    51         9    49.0        55.6   0.0000  1.000    1.000   2.624e-01
11:01    55         5    50.9        40.0   0.0000  1.000    1.000  -4.418e-01
23:01    58         2    50.0        50.0   0.0000  1.000    1.000   2.686e-16
24:03    59         1    50.8         0.0   0.0000  1.000    1.000  -1.560e+01
25:01    55         5    52.7        20.0   0.8727  0.350    0.353  -1.495e+00
26:01    57         3    52.6         0.0   1.4035  0.236    0.237  -1.667e+01
29:02    56         4    51.8        25.0   0.2679  0.605    0.612  -1.170e+00
31:01    57         3    49.1        66.7   0.0000  1.000    1.000   7.282e-01
32:01    56         4    46.4       100.0   2.4107  0.121    0.112   1.771e+01
68:01    57         3    52.6         0.0   1.4035  0.236    0.237  -1.667e+01
          h.2.5%   h.97.5% h.pval
24:02     0.1585    3.4251 0.032*
-----                            
01:01    -1.0330    1.0330 1.000 
02:01    -1.1643    0.8899 0.793 
02:06 -2868.1268 2836.9268 0.991 
03:01    -1.1624    1.6872 0.718 
11:01    -2.3074    1.4237 0.643 
23:01    -2.8192    2.8192 1.000 
24:03 -2868.1268 2836.9268 0.991 
25:01    -3.7498    0.7588 0.194 
26:01 -2731.9621 2698.6192 0.990 
29:02    -3.4931    1.1530 0.324 
31:01    -1.7277    3.1842 0.561 
32:01 -3859.2763 3894.6947 0.993 
68:01 -2731.9621 2698.6192 0.990 
Logistic regression (dominant model) with 60 individuals:
  glm(case ˜ h + pc1, family = binomial, data = data)
      [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p      h.est
24:02    49        11    42.9        81.8   4.0074  0.045*   0.042*  1.793e+00
-----                                                                         
01:01    36        24    50.0        50.0   0.0000  1.000    1.000  -2.268e-04
02:01    25        35    52.0        48.6   0.0000  1.000    1.000  -1.370e-01
02:06    59         1    50.8         0.0   0.0000  1.000    1.000  -1.562e+01
03:01    51         9    49.0        55.6   0.0000  1.000    1.000   2.686e-01
11:01    55         5    50.9        40.0   0.0000  1.000    1.000  -4.451e-01
23:01    58         2    50.0        50.0   0.0000  1.000    1.000  -3.062e-03
24:03    59         1    50.8         0.0   0.0000  1.000    1.000  -1.560e+01
25:01    55         5    52.7        20.0   0.8727  0.350    0.353  -1.501e+00
26:01    57         3    52.6         0.0   1.4035  0.236    0.237  -1.667e+01
29:02    56         4    51.8        25.0   0.2679  0.605    0.612  -1.189e+00
31:01    57         3    49.1        66.7   0.0000  1.000    1.000   7.289e-01
32:01    56         4    46.4       100.0   2.4107  0.121    0.112   1.781e+01
68:01    57         3    52.6         0.0   1.4035  0.236    0.237  -1.673e+01
          h.2.5%   h.97.5% h.pval   pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02     0.1587    3.4264 0.032*  0.011111  -0.5249    0.5471   0.968 
-----                                                                  
01:01    -1.0334    1.0330 1.000  -0.005807  -0.5126    0.5010   0.982 
02:01    -1.1652    0.8913 0.794  -0.002618  -0.5102    0.5049   0.992 
02:06 -2868.1460 2836.9076 0.991  -0.028534  -0.5374    0.4803   0.912 
03:01    -1.1813    1.7185 0.717   0.011958  -0.5044    0.5283   0.964 
11:01    -2.3225    1.4322 0.642   0.008025  -0.5026    0.5186   0.975 
23:01    -2.8348    2.8287 0.998  -0.005857  -0.5148    0.5031   0.982 
24:03 -2868.1286 2836.9250 0.991  -0.011249  -0.5182    0.4957   0.965 
25:01    -3.7579    0.7568 0.193  -0.025685  -0.5490    0.4976   0.923 
26:01 -2731.8901 2698.5450 0.990  -0.014069  -0.5297    0.5015   0.957 
29:02    -3.5309    1.1526 0.320   0.033234  -0.4796    0.5461   0.899 
31:01    -1.7274    3.1851 0.561  -0.008320  -0.5153    0.4987   0.974 
32:01 -3845.6317 3881.2510 0.993  -0.125426  -0.6671    0.4162   0.650 
68:01 -2721.2124 2687.7497 0.990  -0.086589  -0.6512    0.4781   0.764 
Logistic regression (dominant model) with 60 individuals:
  glm(case ˜ h + pc1, family = binomial, data = data)
      [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p  h.est_OR
24:02    49        11    42.9        81.8   4.0074  0.045*   0.042* 6.005e+00
-----                                                                        
01:01    36        24    50.0        50.0   0.0000  1.000    1.000  9.998e-01
02:01    25        35    52.0        48.6   0.0000  1.000    1.000  8.720e-01
02:06    59         1    50.8         0.0   0.0000  1.000    1.000  1.647e-07
03:01    51         9    49.0        55.6   0.0000  1.000    1.000  1.308e+00
11:01    55         5    50.9        40.0   0.0000  1.000    1.000  6.407e-01
23:01    58         2    50.0        50.0   0.0000  1.000    1.000  9.969e-01
24:03    59         1    50.8         0.0   0.0000  1.000    1.000  1.676e-07
25:01    55         5    52.7        20.0   0.8727  0.350    0.353  2.230e-01
26:01    57         3    52.6         0.0   1.4035  0.236    0.237  5.744e-08
29:02    56         4    51.8        25.0   0.2679  0.605    0.612  3.045e-01
31:01    57         3    49.1        66.7   0.0000  1.000    1.000  2.073e+00
32:01    56         4    46.4       100.0   2.4107  0.121    0.112  5.428e+07
68:01    57         3    52.6         0.0   1.4035  0.236    0.237  5.416e-08
      h.2.5%_OR h.97.5%_OR h.pval   pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02   1.17200     30.766 0.032*  0.011111  -0.5249    0.5471   0.968 
-----                                                                  
01:01   0.35579      2.809 1.000  -0.005807  -0.5126    0.5010   0.982 
02:01   0.31185      2.438 0.794  -0.002618  -0.5102    0.5049   0.992 
02:06   0.00000        Inf 0.991  -0.028534  -0.5374    0.4803   0.912 
03:01   0.30687      5.576 0.717   0.011958  -0.5044    0.5283   0.964 
11:01   0.09803      4.188 0.642   0.008025  -0.5026    0.5186   0.975 
23:01   0.05873     16.923 0.998  -0.005857  -0.5148    0.5031   0.982 
24:03   0.00000        Inf 0.991  -0.011249  -0.5182    0.4957   0.965 
25:01   0.02333      2.131 0.193  -0.025685  -0.5490    0.4976   0.923 
26:01   0.00000        Inf 0.990  -0.014069  -0.5297    0.5015   0.957 
29:02   0.02928      3.167 0.320   0.033234  -0.4796    0.5461   0.899 
31:01   0.17774     24.171 0.561  -0.008320  -0.5153    0.4987   0.974 
32:01   0.00000        Inf 0.993  -0.125426  -0.6671    0.4162   0.650 
68:01   0.00000        Inf 0.990  -0.086589  -0.6512    0.4781   0.764 
Linear regression (dominant model) with 60 individuals:
  glm(y ˜ h, data = data)
      [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p    h.est  h.2.5% h.97.5%
01:01    36        24  -0.14684     -0.117427  0.909   0.02941 -0.4805  0.5393
02:01    25        35  -0.32331     -0.000618  0.190   0.32269 -0.1772  0.8226
02:06    59         1  -0.14024      0.170057       .  0.31030 -1.6397  2.2603
03:01    51         9  -0.05600     -0.583178  0.147  -0.52718 -1.2136  0.1592
11:01    55         5  -0.19188      0.489815  0.287   0.68170 -0.2051  1.5685
23:01    58         2  -0.15400      0.413687  0.281   0.56768 -0.8165  1.9518
24:02    49        11  -0.10486     -0.269664  0.537  -0.16481 -0.8091  0.4795
24:03    59         1  -0.11409     -1.373118       . -1.25903 -3.1835  0.6655
25:01    55         5  -0.12237     -0.274749  0.742  -0.15237 -1.0555  0.7507
26:01    57         3  -0.12473     -0.331558  0.690  -0.20683 -1.3519  0.9383
29:02    56         4  -0.13044     -0.199941  0.789  -0.06950 -1.0709  0.9319
31:01    57         3  -0.10097     -0.783003  0.607  -0.68203 -1.8149  0.4508
32:01    56         4  -0.07702     -0.947791  0.092  -0.87077 -1.8470  0.1054
68:01    57         3  -0.16915      0.512457  0.196   0.68161 -0.4512  1.8145
      h.pval
01:01 0.910 
02:01 0.211 
02:06 0.756 
03:01 0.138 
11:01 0.137 
23:01 0.425 
24:02 0.618 
24:03 0.205 
25:01 0.742 
26:01 0.725 
29:02 0.892 
31:01 0.243 
32:01 0.086 
68:01 0.243 
Linear regression (dominant model) with 60 individuals:
  glm(y ˜ h + pc1, data = data)
      [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p    h.est  h.2.5%
01:01    36        24  -0.14684     -0.117427  0.909   0.03377 -0.4773
02:01    25        35  -0.32331     -0.000618  0.190   0.31273 -0.1891
02:06    59         1  -0.14024      0.170057       .  0.38821 -1.5722
03:01    51         9  -0.05600     -0.583178  0.147  -0.48613 -1.1884
11:01    55         5  -0.19188      0.489815  0.287   0.64430 -0.2520
23:01    58         2  -0.15400      0.413687  0.281   0.63150 -0.7598
24:02    49        11  -0.10486     -0.269664  0.537  -0.15742 -0.8034
24:03    59         1  -0.11409     -1.373118       . -1.24145 -3.1708
25:01    55         5  -0.12237     -0.274749  0.742  -0.13241 -1.0388
26:01    57         3  -0.12473     -0.331558  0.690  -0.19823 -1.3460
29:02    56         4  -0.13044     -0.199941  0.789  -0.13606 -1.1496
31:01    57         3  -0.10097     -0.783003  0.607  -0.69057 -1.8254
32:01    56         4  -0.07702     -0.947791  0.092  -0.99595 -1.9862
68:01    57         3  -0.16915      0.512457  0.196   0.76795 -0.3749
        h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01  0.544844 0.897  0.11172  -0.1390    0.3624   0.386 
02:01  0.814606 0.227  0.10412  -0.1436    0.3519   0.414 
02:06  2.348616 0.699  0.11570  -0.1356    0.3670   0.371 
03:01  0.216142 0.180  0.07919  -0.1719    0.3303   0.539 
11:01  1.540569 0.164  0.09117  -0.1569    0.3392   0.474 
23:01  2.022811 0.377  0.12207  -0.1280    0.3721   0.343 
24:02  0.488543 0.635  0.10982  -0.1404    0.3601   0.393 
24:03  0.687920 0.212  0.10809  -0.1392    0.3554   0.395 
25:01  0.773943 0.776  0.10956  -0.1413    0.3604   0.396 
26:01  0.949529 0.736  0.11067  -0.1398    0.3611   0.390 
29:02  0.877431 0.793  0.11626  -0.1369    0.3694   0.372 
31:01  0.444260 0.238  0.11387  -0.1338    0.3615   0.371 
32:01 -0.005739 0.054  0.16001  -0.0873    0.4073   0.210 
68:01  1.910822 0.193  0.13482  -0.1146    0.3842   0.294 
Linear regression (dominant model) with 60 individuals:
  glm(y ˜ h + pc1, data = data)
      [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p    h.est  h.2.5%
01:01    36        24  -0.14684     -0.117427  0.909   0.03377 -0.4773
02:01    25        35  -0.32331     -0.000618  0.190   0.31273 -0.1891
02:06    59         1  -0.14024      0.170057       .  0.38821 -1.5722
03:01    51         9  -0.05600     -0.583178  0.147  -0.48613 -1.1884
11:01    55         5  -0.19188      0.489815  0.287   0.64430 -0.2520
23:01    58         2  -0.15400      0.413687  0.281   0.63150 -0.7598
24:02    49        11  -0.10486     -0.269664  0.537  -0.15742 -0.8034
24:03    59         1  -0.11409     -1.373118       . -1.24145 -3.1708
25:01    55         5  -0.12237     -0.274749  0.742  -0.13241 -1.0388
26:01    57         3  -0.12473     -0.331558  0.690  -0.19823 -1.3460
29:02    56         4  -0.13044     -0.199941  0.789  -0.13606 -1.1496
31:01    57         3  -0.10097     -0.783003  0.607  -0.69057 -1.8254
32:01    56         4  -0.07702     -0.947791  0.092  -0.99595 -1.9862
68:01    57         3  -0.16915      0.512457  0.196   0.76795 -0.3749
        h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01  0.544844 0.897  0.11172  -0.1390    0.3624   0.386 
02:01  0.814606 0.227  0.10412  -0.1436    0.3519   0.414 
02:06  2.348616 0.699  0.11570  -0.1356    0.3670   0.371 
03:01  0.216142 0.180  0.07919  -0.1719    0.3303   0.539 
11:01  1.540569 0.164  0.09117  -0.1569    0.3392   0.474 
23:01  2.022811 0.377  0.12207  -0.1280    0.3721   0.343 
24:02  0.488543 0.635  0.10982  -0.1404    0.3601   0.393 
24:03  0.687920 0.212  0.10809  -0.1392    0.3554   0.395 
25:01  0.773943 0.776  0.10956  -0.1413    0.3604   0.396 
26:01  0.949529 0.736  0.11067  -0.1398    0.3611   0.390 
29:02  0.877431 0.793  0.11626  -0.1369    0.3694   0.372 
31:01  0.444260 0.238  0.11387  -0.1338    0.3615   0.371 
32:01 -0.005739 0.054  0.16001  -0.0873    0.4073   0.210 
68:01  1.910822 0.193  0.13482  -0.1146    0.3842   0.294 
Logistic regression (additive model) with 60 individuals:
  glm(case ˜ h, family = binomial, data = data)
      [-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p    h.est     h.2.5%
24:02 109  11  46.8  81.8   3.6030  0.058    0.053    1.7918     0.1585
-----                                                                  
01:01  95  25  50.5  48.0   0.0000  1.000    1.000   -0.1207    -1.0843
02:01  77  43  48.1  53.5   0.1450  0.703    0.704    0.2137    -0.5289
02:06 119   1  50.4   0.0   0.0000  1.000    1.000  -15.6000 -2868.1268
03:01 111   9  49.5  55.6   0.0000  1.000    1.000    0.2624    -1.1624
11:01 115   5  50.4  40.0   0.0000  1.000    1.000   -0.4418    -2.3074
23:01 117   3  50.4  33.3   0.0000  1.000    1.000   -0.4323    -2.3435
24:03 119   1  50.4   0.0   0.0000  1.000    1.000  -15.6000 -2868.1268
25:01 115   5  51.3  20.0   0.8348  0.361    0.364   -1.4955    -3.7498
26:01 117   3  51.3   0.0   1.3675  0.242    0.244  -16.6714 -2731.9621
29:02 116   4  50.9  25.0   0.2586  0.611    0.619   -1.1701    -3.4931
31:01 117   3  49.6  66.7   0.0000  1.000    1.000    0.7282    -1.7277
32:01 116   4  48.3 100.0   2.3276  0.127    0.119   17.7092 -3859.2763
68:01 117   3  51.3   0.0   1.3675  0.242    0.244  -16.6714 -2731.9621
        h.97.5% h.pval
24:02    3.4251 0.032*
-----                 
01:01    0.8430 0.806 
02:01    0.9563 0.573 
02:06 2836.9268 0.991 
03:01    1.6872 0.718 
11:01    1.4237 0.643 
23:01    1.4789 0.658 
24:03 2836.9268 0.991 
25:01    0.7588 0.194 
26:01 2698.6192 0.990 
29:02    1.1530 0.324 
31:01    3.1842 0.561 
32:01 3894.6947 0.993 
68:01 2698.6192 0.990 
Logistic regression (recessive model) with 60 individuals:
  glm(case ˜ h, family = binomial, data = data)
      [-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p   h.est
01:01        59     1        50.8       0    0.000  1.000    1.000  -15.600
02:01        52     8        46.2      75    1.298  0.255    0.254    1.253
02:06        60     0        50.0       .        .       .        .       .
03:01        60     0        50.0       .        .       .        .       .
11:01        60     0        50.0       .        .       .        .       .
23:01        59     1        50.8       0    0.000  1.000    1.000  -15.600
24:02        60     0        50.0       .        .       .        .       .
24:03        60     0        50.0       .        .       .        .       .
25:01        60     0        50.0       .        .       .        .       .
26:01        60     0        50.0       .        .       .        .       .
29:02        60     0        50.0       .        .       .        .       .
31:01        60     0        50.0       .        .       .        .       .
32:01        60     0        50.0       .        .       .        .       .
68:01        60     0        50.0       .        .       .        .       .
          h.2.5%  h.97.5% h.pval
01:01 -2868.1268 2836.927 0.991 
02:01    -0.4379    2.943 0.146 
02:06          .        .      .
03:01          .        .      .
11:01          .        .      .
23:01 -2868.1268 2836.927 0.991 
24:02          .        .      .
24:03          .        .      .
25:01          .        .      .
26:01          .        .      .
29:02          .        .      .
31:01          .        .      .
32:01          .        .      .
68:01          .        .      .
Logistic regression (genotype model) with 60 individuals:
  glm(case ˜ h, family = binomial, data = data)
      [-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02    49    11     0    42.9    81.8       .   4.0074  0.045*   0.042*
-----                                                                    
01:01    36    23     1    50.0    52.2       0   1.0435  0.593    1.000 
02:01    25    27     8    52.0    40.7      75   2.9659  0.227    0.271 
02:06    59     1     0    50.8     0.0       .   0.0000  1.000    1.000 
03:01    51     9     0    49.0    55.6       .   0.0000  1.000    1.000 
11:01    55     5     0    50.9    40.0       .   0.0000  1.000    1.000 
23:01    58     1     1    50.0   100.0       0   2.0000  0.368    1.000 
24:03    59     1     0    50.8     0.0       .   0.0000  1.000    1.000 
25:01    55     5     0    52.7    20.0       .   0.8727  0.350    0.353 
26:01    57     3     0    52.6     0.0       .   1.4035  0.236    0.237 
29:02    56     4     0    51.8    25.0       .   0.2679  0.605    0.612 
31:01    57     3     0    49.1    66.7       .   0.0000  1.000    1.000 
32:01    56     4     0    46.4   100.0       .   2.4107  0.121    0.112 
68:01    57     3     0    52.6     0.0       .   1.4035  0.236    0.237 
         h1.est    h1.2.5%  h1.97.5% h1.pval  h2.est    h2.2.5% h2.97.5%
24:02   1.79176     0.1585    3.4251  0.032*       .          .        .
-----                                                                   
01:01   0.08701    -0.9600    1.1340  0.871  -15.566 -2868.0929 2836.961
02:01  -0.45474    -1.5524    0.6430  0.417    1.019    -0.7637    2.801
02:06 -15.59997 -2868.1268 2836.9268  0.991        .          .        .
03:01   0.26236    -1.1624    1.6872  0.718        .          .        .
11:01  -0.44183    -2.3074    1.4237  0.643        .          .        .
23:01  16.56607 -4686.4552 4719.5873  0.994  -16.566 -4719.5873 4686.455
24:03 -15.59997 -2868.1268 2836.9268  0.991        .          .        .
25:01  -1.49549    -3.7498    0.7588  0.194        .          .        .
26:01 -16.67143 -2731.9621 2698.6192  0.990        .          .        .
29:02  -1.17007    -3.4931    1.1530  0.324        .          .        .
31:01   0.72824    -1.7277    3.1842  0.561        .          .        .
32:01  17.70917 -3859.2763 3894.6947  0.993        .          .        .
68:01 -16.67143 -2731.9621 2698.6192  0.990        .          .        .
      h2.pval
24:02       .
-----        
01:01  0.991 
02:01  0.263 
02:06       .
03:01       .
11:01       .
23:01  0.994 
24:03       .
25:01       .
26:01       .
29:02       .
31:01       .
32:01       .
68:01       .
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:30
     1, SNP: 155, Loss: 186.198, OOB Acc: 68.18%, # of Haplo: 13
     2, SNP: 111, Loss: 138.902, OOB Acc: 72.73%, # of Haplo: 13
     3, SNP: 159, Loss: 134.624, OOB Acc: 81.82%, # of Haplo: 15
     4, SNP: 75, Loss: 120.53, OOB Acc: 86.36%, # of Haplo: 16
     5, SNP: 129, Loss: 79.3436, OOB Acc: 90.91%, # of Haplo: 20
     6, SNP: 135, Loss: 56.5856, OOB Acc: 90.91%, # of Haplo: 20
     7, SNP: 162, Loss: 40.1347, OOB Acc: 90.91%, # of Haplo: 20
     8, SNP: 192, Loss: 29.7565, OOB Acc: 90.91%, # of Haplo: 24
     9, SNP: 182, Loss: 23.1586, OOB Acc: 90.91%, # of Haplo: 24
    10, SNP: 24, Loss: 16.7007, OOB Acc: 90.91%, # of Haplo: 27
    11, SNP: 154, Loss: 15.6368, OOB Acc: 90.91%, # of Haplo: 28
    12, SNP: 17, Loss: 15.4059, OOB Acc: 90.91%, # of Haplo: 39
    13, SNP: 199, Loss: 14.4894, OOB Acc: 90.91%, # of Haplo: 40
    14, SNP: 73, Loss: 8.03154, OOB Acc: 90.91%, # of Haplo: 42
    15, SNP: 151, Loss: 5.75619, OOB Acc: 90.91%, # of Haplo: 42
[1] 2019-04-13 03:03:30, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
     1, SNP: 87, Loss: 163.788, OOB Acc: 50.00%, # of Haplo: 15
     2, SNP: 179, Loss: 159.036, OOB Acc: 57.69%, # of Haplo: 16
     3, SNP: 162, Loss: 127.744, OOB Acc: 61.54%, # of Haplo: 17
     4, SNP: 113, Loss: 120.348, OOB Acc: 76.92%, # of Haplo: 17
     5, SNP: 149, Loss: 99.7027, OOB Acc: 80.77%, # of Haplo: 19
     6, SNP: 183, Loss: 63.9201, OOB Acc: 80.77%, # of Haplo: 19
     7, SNP: 82, Loss: 46.9454, OOB Acc: 80.77%, # of Haplo: 19
     8, SNP: 73, Loss: 36.2707, OOB Acc: 84.62%, # of Haplo: 21
     9, SNP: 47, Loss: 34.9876, OOB Acc: 84.62%, # of Haplo: 22
    10, SNP: 140, Loss: 27.9494, OOB Acc: 84.62%, # of Haplo: 22
    11, SNP: 189, Loss: 23.9141, OOB Acc: 84.62%, # of Haplo: 23
    12, SNP: 249, Loss: 18.884, OOB Acc: 84.62%, # of Haplo: 27
    13, SNP: 151, Loss: 15.8511, OOB Acc: 84.62%, # of Haplo: 27
    14, SNP: 21, Loss: 11.1982, OOB Acc: 88.46%, # of Haplo: 30
    15, SNP: 215, Loss: 10.7762, OOB Acc: 88.46%, # of Haplo: 32
    16, SNP: 136, Loss: 10.5472, OOB Acc: 88.46%, # of Haplo: 38
    17, SNP: 117, Loss: 10.3045, OOB Acc: 88.46%, # of Haplo: 38
    18, SNP: 106, Loss: 10.3041, OOB Acc: 92.31%, # of Haplo: 38
    19, SNP: 199, Loss: 9.5325, OOB Acc: 92.31%, # of Haplo: 38
[2] 2019-04-13 03:03:30, OOB Acc: 92.31%, # of SNPs: 19, # of Haplo: 38
     1, SNP: 135, Loss: 156.001, OOB Acc: 50.00%, # of Haplo: 13
     2, SNP: 141, Loss: 121.763, OOB Acc: 59.09%, # of Haplo: 14
     3, SNP: 162, Loss: 110.134, OOB Acc: 63.64%, # of Haplo: 14
     4, SNP: 73, Loss: 95.625, OOB Acc: 72.73%, # of Haplo: 15
     5, SNP: 96, Loss: 87.0485, OOB Acc: 86.36%, # of Haplo: 16
     6, SNP: 129, Loss: 49.5862, OOB Acc: 86.36%, # of Haplo: 18
     7, SNP: 147, Loss: 27.5253, OOB Acc: 86.36%, # of Haplo: 19
     8, SNP: 105, Loss: 16.9538, OOB Acc: 86.36%, # of Haplo: 19
     9, SNP: 258, Loss: 11.9891, OOB Acc: 86.36%, # of Haplo: 19
    10, SNP: 160, Loss: 10.5954, OOB Acc: 86.36%, # of Haplo: 20
    11, SNP: 199, Loss: 9.81573, OOB Acc: 86.36%, # of Haplo: 20
    12, SNP: 151, Loss: 9.3333, OOB Acc: 86.36%, # of Haplo: 21
[3] 2019-04-13 03:03:31, OOB Acc: 86.36%, # of SNPs: 12, # of Haplo: 21
     1, SNP: 184, Loss: 221.947, OOB Acc: 63.64%, # of Haplo: 15
     2, SNP: 87, Loss: 159.682, OOB Acc: 77.27%, # of Haplo: 18
     3, SNP: 84, Loss: 135.191, OOB Acc: 81.82%, # of Haplo: 21
     4, SNP: 36, Loss: 128.841, OOB Acc: 86.36%, # of Haplo: 23
     5, SNP: 109, Loss: 94.3071, OOB Acc: 90.91%, # of Haplo: 27
     6, SNP: 130, Loss: 66.1551, OOB Acc: 90.91%, # of Haplo: 29
     7, SNP: 64, Loss: 50.915, OOB Acc: 90.91%, # of Haplo: 29
     8, SNP: 194, Loss: 41.1566, OOB Acc: 90.91%, # of Haplo: 29
     9, SNP: 193, Loss: 32.6689, OOB Acc: 90.91%, # of Haplo: 30
    10, SNP: 123, Loss: 30.6255, OOB Acc: 90.91%, # of Haplo: 30
    11, SNP: 55, Loss: 30.1894, OOB Acc: 90.91%, # of Haplo: 35
    12, SNP: 95, Loss: 29.8967, OOB Acc: 90.91%, # of Haplo: 43
    13, SNP: 151, Loss: 25.6379, OOB Acc: 90.91%, # of Haplo: 43
    14, SNP: 199, Loss: 24.4761, OOB Acc: 90.91%, # of Haplo: 44
[4] 2019-04-13 03:03:31, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 44
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002704645 0.0002740287 0.0003061065 0.0041563208 0.0120425968 0.0259547286 
        Max.         Mean           SD 
0.3993959563 0.0433199485 0.0939557543 
Accuracy with training data: 98.5%
Out-of-bag accuracy: 90.1%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 47
    avg. # of SNPs in an individual classifier: 15.00
        (sd: 2.94, min: 12, max: 19, median: 14.50)
    avg. # of haplotypes in an individual classifier: 36.25
        (sd: 10.47, min: 21, max: 44, median: 40.00)
    avg. out-of-bag accuracy: 90.12%
        (sd: 2.59%, min: 86.36%, max: 92.31%, median: 90.91%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002704645 0.0002740287 0.0003061065 0.0041563208 0.0120425968 0.0259547286 
        Max.         Mean           SD 
0.3993959563 0.0433199485 0.0939557543 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:31)	0%
Predicting (2019-04-13 03:03:31)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.8%)   1 (3.8%)  8 (30.8%) 16 (61.5%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0009577 0.0048947 0.0311502 0.0380943 0.3993960 
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:31)	0%
Predicting (2019-04-13 03:03:31)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.8%)   1 (3.8%)  8 (30.8%) 16 (61.5%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0009577 0.0048947 0.0311502 0.0380943 0.3993960 
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
SNP genotypes: 
    90 samples X 3932 SNPs
    SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
    min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
    min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
 A/G  C/T  G/T  A/C  C/G  A/T 
1567 1510  348  332  111   64 
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 5316 SNPs from chromosome 6.
SNP genotypes: 
    90 samples X 5316 SNPs
    SNPs range from 25651262bp to 33426848bp on hg19
Missing rate per SNP:
    min: 0, max: 0.1, mean: 0.0882054, median: 0.1, sd: 0.030674
Missing rate per sample:
    min: 0, max: 0.863619, mean: 0.0882054, median: 0.00131678, sd: 0.259735
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.201867, median: 0.179012, sd: 0.155475
Allelic information:
 A/G  C/T  G/T  A/C  C/G  A/T 
2102 2046  480  471  134   83 
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77, # of samples: 60
# of unique HLA alleles: 12
[-] 2019-04-13 03:03:31
[1] 2019-04-13 03:03:31, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20
[2] 2019-04-13 03:03:31, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 
        Max.         Mean           SD 
4.728168e-01 4.409445e-02 1.068815e-01 
Accuracy with training data: 95.0%
Out-of-bag accuracy: 94.5%
Gene: DQB1
Training dataset: 60 samples X 77 SNPs
    # of HLA alleles: 12
    # of individual classifiers: 2
    total # of SNPs used: 20
    avg. # of SNPs in an individual classifier: 14.00
        (sd: 1.41, min: 13, max: 15, median: 14.00)
    avg. # of haplotypes in an individual classifier: 20.50
        (sd: 0.71, min: 20, max: 21, median: 20.50)
    avg. out-of-bag accuracy: 94.45%
        (sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 
        Max.         Mean           SD 
4.728168e-01 4.409445e-02 1.068815e-01 
Genome assembly: hg19
The HIBAG model:
	There are 77 SNP predictors in total.
	There are 2 individual classifiers.
Summarize the missing fractions of SNP predictors per classifier:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0       0       0       0       0       0 
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 60
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 0
# of unique HLA genotypes: 0
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 200
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Exclude 9 monomorphic SNPs
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:31
[1] 2019-04-13 03:03:31, OOB Acc: 86.96%, # of SNPs: 11, # of Haplo: 48
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.898504e-09 2.973325e-09 3.646720e-09 1.495845e-03 1.516334e-02 6.687055e-02 
        Max.         Mean           SD 
9.834029e-01 9.608437e-02 2.420052e-01 
Accuracy with training data: 92.5%
Out-of-bag accuracy: 87.0%
Exclude 9 monomorphic SNPs
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:31
[1] 2019-04-13 03:03:31, OOB Acc: 82.61%, # of SNPs: 14, # of Haplo: 54
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
6.946528e-15 6.575829e-14 5.950641e-13 1.660680e-03 6.378961e-03 1.518505e-02 
        Max.         Mean           SD 
6.400032e-01 4.484927e-02 1.420419e-01 
Accuracy with training data: 92.5%
Out-of-bag accuracy: 82.6%
Gene: A
Training dataset: 60 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 2
    total # of SNPs used: 23
    avg. # of SNPs in an individual classifier: 12.50
        (sd: 2.12, min: 11, max: 14, median: 12.50)
    avg. # of haplotypes in an individual classifier: 51.00
        (sd: 4.24, min: 48, max: 54, median: 51.00)
    avg. out-of-bag accuracy: 84.78%
        (sd: 3.07%, min: 82.61%, max: 86.96%, median: 84.78%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
1.006377e-08 1.077445e-08 1.717056e-08 3.680759e-03 1.272457e-02 4.469180e-02 
        Max.         Mean           SD 
8.117031e-01 7.046682e-02 1.896453e-01 
Genome assembly: hg19
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:32
     1, SNP: 155, Loss: 186.198, OOB Acc: 68.18%, # of Haplo: 13
     2, SNP: 111, Loss: 138.902, OOB Acc: 72.73%, # of Haplo: 13
     3, SNP: 159, Loss: 134.624, OOB Acc: 81.82%, # of Haplo: 15
     4, SNP: 75, Loss: 120.53, OOB Acc: 86.36%, # of Haplo: 16
     5, SNP: 129, Loss: 79.3436, OOB Acc: 90.91%, # of Haplo: 20
     6, SNP: 135, Loss: 56.5856, OOB Acc: 90.91%, # of Haplo: 20
     7, SNP: 162, Loss: 40.1347, OOB Acc: 90.91%, # of Haplo: 20
     8, SNP: 192, Loss: 29.7565, OOB Acc: 90.91%, # of Haplo: 24
     9, SNP: 182, Loss: 23.1586, OOB Acc: 90.91%, # of Haplo: 24
    10, SNP: 24, Loss: 16.7007, OOB Acc: 90.91%, # of Haplo: 27
    11, SNP: 154, Loss: 15.6368, OOB Acc: 90.91%, # of Haplo: 28
    12, SNP: 17, Loss: 15.4059, OOB Acc: 90.91%, # of Haplo: 39
    13, SNP: 199, Loss: 14.4894, OOB Acc: 90.91%, # of Haplo: 40
    14, SNP: 73, Loss: 8.03154, OOB Acc: 90.91%, # of Haplo: 42
    15, SNP: 151, Loss: 5.75619, OOB Acc: 90.91%, # of Haplo: 42
[1] 2019-04-13 03:03:32, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
     1, SNP: 87, Loss: 163.788, OOB Acc: 50.00%, # of Haplo: 15
     2, SNP: 179, Loss: 159.036, OOB Acc: 57.69%, # of Haplo: 16
     3, SNP: 162, Loss: 127.744, OOB Acc: 61.54%, # of Haplo: 17
     4, SNP: 113, Loss: 120.348, OOB Acc: 76.92%, # of Haplo: 17
     5, SNP: 149, Loss: 99.7027, OOB Acc: 80.77%, # of Haplo: 19
     6, SNP: 183, Loss: 63.9201, OOB Acc: 80.77%, # of Haplo: 19
     7, SNP: 82, Loss: 46.9454, OOB Acc: 80.77%, # of Haplo: 19
     8, SNP: 73, Loss: 36.2707, OOB Acc: 84.62%, # of Haplo: 21
     9, SNP: 47, Loss: 34.9876, OOB Acc: 84.62%, # of Haplo: 22
    10, SNP: 140, Loss: 27.9494, OOB Acc: 84.62%, # of Haplo: 22
    11, SNP: 189, Loss: 23.9141, OOB Acc: 84.62%, # of Haplo: 23
    12, SNP: 249, Loss: 18.884, OOB Acc: 84.62%, # of Haplo: 27
    13, SNP: 151, Loss: 15.8511, OOB Acc: 84.62%, # of Haplo: 27
    14, SNP: 21, Loss: 11.1982, OOB Acc: 88.46%, # of Haplo: 30
    15, SNP: 215, Loss: 10.7762, OOB Acc: 88.46%, # of Haplo: 32
    16, SNP: 136, Loss: 10.5472, OOB Acc: 88.46%, # of Haplo: 38
    17, SNP: 117, Loss: 10.3045, OOB Acc: 88.46%, # of Haplo: 38
    18, SNP: 106, Loss: 10.3041, OOB Acc: 92.31%, # of Haplo: 38
    19, SNP: 199, Loss: 9.5325, OOB Acc: 92.31%, # of Haplo: 38
[2] 2019-04-13 03:03:32, OOB Acc: 92.31%, # of SNPs: 19, # of Haplo: 38
     1, SNP: 135, Loss: 156.001, OOB Acc: 50.00%, # of Haplo: 13
     2, SNP: 141, Loss: 121.763, OOB Acc: 59.09%, # of Haplo: 14
     3, SNP: 162, Loss: 110.134, OOB Acc: 63.64%, # of Haplo: 14
     4, SNP: 73, Loss: 95.625, OOB Acc: 72.73%, # of Haplo: 15
     5, SNP: 96, Loss: 87.0485, OOB Acc: 86.36%, # of Haplo: 16
     6, SNP: 129, Loss: 49.5862, OOB Acc: 86.36%, # of Haplo: 18
     7, SNP: 147, Loss: 27.5253, OOB Acc: 86.36%, # of Haplo: 19
     8, SNP: 105, Loss: 16.9538, OOB Acc: 86.36%, # of Haplo: 19
     9, SNP: 258, Loss: 11.9891, OOB Acc: 86.36%, # of Haplo: 19
    10, SNP: 160, Loss: 10.5954, OOB Acc: 86.36%, # of Haplo: 20
    11, SNP: 199, Loss: 9.81573, OOB Acc: 86.36%, # of Haplo: 20
    12, SNP: 151, Loss: 9.3333, OOB Acc: 86.36%, # of Haplo: 21
[3] 2019-04-13 03:03:32, OOB Acc: 86.36%, # of SNPs: 12, # of Haplo: 21
     1, SNP: 184, Loss: 221.947, OOB Acc: 63.64%, # of Haplo: 15
     2, SNP: 87, Loss: 159.682, OOB Acc: 77.27%, # of Haplo: 18
     3, SNP: 84, Loss: 135.191, OOB Acc: 81.82%, # of Haplo: 21
     4, SNP: 36, Loss: 128.841, OOB Acc: 86.36%, # of Haplo: 23
     5, SNP: 109, Loss: 94.3071, OOB Acc: 90.91%, # of Haplo: 27
     6, SNP: 130, Loss: 66.1551, OOB Acc: 90.91%, # of Haplo: 29
     7, SNP: 64, Loss: 50.915, OOB Acc: 90.91%, # of Haplo: 29
     8, SNP: 194, Loss: 41.1566, OOB Acc: 90.91%, # of Haplo: 29
     9, SNP: 193, Loss: 32.6689, OOB Acc: 90.91%, # of Haplo: 30
    10, SNP: 123, Loss: 30.6255, OOB Acc: 90.91%, # of Haplo: 30
    11, SNP: 55, Loss: 30.1894, OOB Acc: 90.91%, # of Haplo: 35
    12, SNP: 95, Loss: 29.8967, OOB Acc: 90.91%, # of Haplo: 43
    13, SNP: 151, Loss: 25.6379, OOB Acc: 90.91%, # of Haplo: 43
    14, SNP: 199, Loss: 24.4761, OOB Acc: 90.91%, # of Haplo: 44
[4] 2019-04-13 03:03:32, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 44
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002704645 0.0002740287 0.0003061065 0.0041563208 0.0120425968 0.0259547286 
        Max.         Mean           SD 
0.3993959563 0.0433199485 0.0939557543 
Accuracy with training data: 98.5%
Out-of-bag accuracy: 90.1%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 47
    avg. # of SNPs in an individual classifier: 15.00
        (sd: 2.94, min: 12, max: 19, median: 14.50)
    avg. # of haplotypes in an individual classifier: 36.25
        (sd: 10.47, min: 21, max: 44, median: 40.00)
    avg. out-of-bag accuracy: 90.12%
        (sd: 2.59%, min: 86.36%, max: 92.31%, median: 90.91%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002704645 0.0002740287 0.0003061065 0.0041563208 0.0120425968 0.0259547286 
        Max.         Mean           SD 
0.3993959563 0.0433199485 0.0939557543 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:32)	0%
Predicting (2019-04-13 03:03:32)	100%
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 09:01
Allelic ambiguity: 09:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num   *   -   A   D   E   F   G   H   I   K   L   M   N   Q   R   S   T   V   W   Y 
  1 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  9 120   .  81   .   .   .   .   .   .   .   .   .   .   .   .   .  15   7   .   .  17 
 44 120   .  25   .   .   .   .   .   .   .   .   .   .   .   .  95   .   .   .   .   . 
 56 120   . 117   .   .   .   .   .   .   .   .   .   .   .   .   3   .   .   .   .   . 
 62 120   .  46   .   .  15   .  44   .   .   .   4   .   .   .  11   .   .   .   .   . 
 63 120   . 105   .   .   .   .   .   .   .   .   .   .  11   4   .   .   .   .   .   . 
 65 120   . 105   .   .   .   .  15   .   .   .   .   .   .   .   .   .   .   .   .   . 
 66 120   .  61   .   .   .   .   .   .   .  59   .   .   .   .   .   .   .   .   .   . 
 67 120   .  25   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  95   .   . 
 70 120   .  99   .   .   .   .   .   .   .   .   .   .   .  21   .   .   .   .   .   . 
 73 120   . 117   .   .   .   .   .   .   3   .   .   .   .   .   .   .   .   .   .   . 
 74 120   .  76   .   .   .   .   .  44   .   .   .   .   .   .   .   .   .   .   .   . 
 76 120   .  32   .   .  24   .   .   .   .   .   .   .   .   .   .   .   .  64   .   . 
 77 120   .  47   .  64   .   .   .   .   .   .   .   .   .   .   .   9   .   .   .   . 
 79 120   .  96   .   .   .   .   .   .   .   .   .   .   .   .  24   .   .   .   .   . 
 80 120   .  96   .   .   .   .   .   .  24   .   .   .   .   .   .   .   .   .   .   . 
 81 120   .  96  24   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 82 120   .  96   .   .   .   .   .   .   .   .  24   .   .   .   .   .   .   .   .   . 
 83 120   .  96   .   .   .   .   .   .   .   .   .   .   .   .  24   .   .   .   .   . 
 90 120   .  38  82   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 95 120   .  61   .   .   .   .   .   .   .   .  15   .   .   .   .   .   .  44   .   . 
 97 120   .  39   .   .   .   .   .   .   .   .   .  29   .   .  52   .   .   .   .   . 
 99 120   . 105   .   .   .  15   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
105 120   .  42   .   .   .   .   .   .   .   .   .   .   .   .   .  78   .   .   .   . 
107 120   .  76   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  44   . 
109 120   . 116   .   .   .   .   .   .   .   .   4   .   .   .   .   .   .   .   .   . 
114 120   .  46   .   .   .   .   .  59   .   .   .   .   .  15   .   .   .   .   .   . 
116 120   .  61   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  59 
127 120   .  58   .   .   .   .   .   .   .  62   .   .   .   .   .   .   .   .   .   . 
142 120   .  73   .   .   .   .   .   .   .   .   .   .   .   .   .   .  47   .   .   . 
144 120   .  98   .   .   .   .   .   .   .   .   .   .   .  22   .   .   .   .   .   . 
145 120   .  73   .   .   .   .   .  47   .   .   .   .   .   .   .   .   .   .   .   . 
149 120   . 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   8   .   .   . 
150 120   .  25  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
151 120   . 106   .   .   .   .   .   .   .   .   .   .   .   .  14   .   .   .   .   . 
152 120   .  30   .   .  17   .   .   .   .   .   .   .   .   .   .   .   .  73   .   . 
156 120   .  25   .   .   .   .   .   .   .   .  67   .   .  17   .   .   .   .  11   . 
158 120   .  25  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
161 120   . 111   .   9   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
163 120   .  38   .   .   .   .   .   .   .   .   .   .   .   .   .   .  82   .   .   . 
166 120   .  39   .   .  81   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
167 120   .  39   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  81   . 
183 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num   *   -   A   D   E   F   G   H   I   K   L   M   N   Q   R   S   T   V   W   Y 
-23 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-22 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-21 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-20 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-19 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-18 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-17 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-16 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-15 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-14 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-13 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-12 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-11 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-10 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -9 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -8 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -7 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -6 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -5 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -4 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -3 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -2 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -1 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  . 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  1 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  9 120   .  81   .   .   .   .   .   .   .   .   .   .   .   .   .  15   7   .   .  17 
 44 120   .  25   .   .   .   .   .   .   .   .   .   .   .   .  95   .   .   .   .   . 
 56 120   . 117   .   .   .   .   .   .   .   .   .   .   .   .   3   .   .   .   .   . 
 62 120   .  46   .   .  15   .  44   .   .   .   4   .   .   .  11   .   .   .   .   . 
 63 120   . 105   .   .   .   .   .   .   .   .   .   .  11   4   .   .   .   .   .   . 
 65 120   . 105   .   .   .   .  15   .   .   .   .   .   .   .   .   .   .   .   .   . 
 66 120   .  61   .   .   .   .   .   .   .  59   .   .   .   .   .   .   .   .   .   . 
 67 120   .  25   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  95   .   . 
 70 120   .  99   .   .   .   .   .   .   .   .   .   .   .  21   .   .   .   .   .   . 
 73 120   . 117   .   .   .   .   .   .   3   .   .   .   .   .   .   .   .   .   .   . 
 74 120   .  76   .   .   .   .   .  44   .   .   .   .   .   .   .   .   .   .   .   . 
 76 120   .  32   .   .  24   .   .   .   .   .   .   .   .   .   .   .   .  64   .   . 
 77 120   .  47   .  64   .   .   .   .   .   .   .   .   .   .   .   9   .   .   .   . 
 79 120   .  96   .   .   .   .   .   .   .   .   .   .   .   .  24   .   .   .   .   . 
 80 120   .  96   .   .   .   .   .   .  24   .   .   .   .   .   .   .   .   .   .   . 
 81 120   .  96  24   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 82 120   .  96   .   .   .   .   .   .   .   .  24   .   .   .   .   .   .   .   .   . 
 83 120   .  96   .   .   .   .   .   .   .   .   .   .   .   .  24   .   .   .   .   . 
 90 120   .  38  82   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 95 120   .  61   .   .   .   .   .   .   .   .  15   .   .   .   .   .   .  44   .   . 
 97 120   .  39   .   .   .   .   .   .   .   .   .  29   .   .  52   .   .   .   .   . 
 99 120   . 105   .   .   .  15   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
105 120   .  42   .   .   .   .   .   .   .   .   .   .   .   .   .  78   .   .   .   . 
107 120   .  76   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  44   . 
109 120   . 116   .   .   .   .   .   .   .   .   4   .   .   .   .   .   .   .   .   . 
114 120   .  46   .   .   .   .   .  59   .   .   .   .   .  15   .   .   .   .   .   . 
116 120   .  61   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  59 
127 120   .  58   .   .   .   .   .   .   .  62   .   .   .   .   .   .   .   .   .   . 
142 120   .  73   .   .   .   .   .   .   .   .   .   .   .   .   .   .  47   .   .   . 
144 120   .  98   .   .   .   .   .   .   .   .   .   .   .  22   .   .   .   .   .   . 
145 120   .  73   .   .   .   .   .  47   .   .   .   .   .   .   .   .   .   .   .   . 
149 120   . 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   8   .   .   . 
150 120   .  25  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
151 120   . 106   .   .   .   .   .   .   .   .   .   .   .   .  14   .   .   .   .   . 
152 120   .  30   .   .  17   .   .   .   .   .   .   .   .   .   .   .   .  73   .   . 
156 120   .  25   .   .   .   .   .   .   .   .  67   .   .  17   .   .   .   .  11   . 
158 120   .  25  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
161 120   . 111   .   9   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
163 120   .  38   .   .   .   .   .   .   .   .   .   .   .   .   .   .  82   .   .   . 
166 120   .  39   .   .  81   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
167 120   .  39   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  81   . 
183 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
184 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
185 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
186 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
187 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
188 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
189 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
190 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
191 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
192 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
193 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
194 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
195 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
196 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
197 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
198 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
199 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
200 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
201 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
202 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
203 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
204 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
205 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
206 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
207 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
208 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
209 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
210 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
211 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
212 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
213 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
214 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
215 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
216 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
217 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
218 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
219 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
220 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
221 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
222 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
223 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
224 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
225 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
226 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
227 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
228 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
229 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
230 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
231 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
232 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
233 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
234 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
235 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
236 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
237 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
238 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
239 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
240 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
241 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
242 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
243 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
244 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
245 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
246 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
247 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
248 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
249 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
250 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
251 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
252 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
253 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
254 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
255 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
256 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
257 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
258 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
259 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
260 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
261 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
262 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
263 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
264 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
265 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
266 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
267 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
268 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
269 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
270 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
271 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
272 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
273 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
274 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
275 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
276 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
277 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
278 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
279 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
280 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
281 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
282 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
283 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
284 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
285 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
286 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
287 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
288 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
289 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
290 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
291 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
292 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
293 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
294 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
295 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
296 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
297 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
298 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
299 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
300 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
301 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
302 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
303 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
304 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
305 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
306 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
307 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
308 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
309 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
310 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
311 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
312 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
313 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
314 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
315 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
316 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
317 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
318 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
319 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
320 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
321 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
322 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
323 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
324 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
325 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
326 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
327 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
328 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
329 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
330 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
331 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
332 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
333 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
334 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
335 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
336 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
337 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
338 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
339 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
340 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
341 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num   *   -   A   D   E   F   G   I   K   L   M   N   P   Q   R   S   T   Y 
  5 120 112   .   .   .   .   .   .   .   .   .   .   8   .   .   .   .   .   . 
  6 120  20  92   8   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  7 112  20  92   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  8 112  20  92   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  9 112   3  76   .   .   .  33   .   .   .   .   .   .   .   .   .   .   .   . 
 10 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 11 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 12 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 13 112   3  93  16   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 14 112   3  14   .   .   .   .   .   .   .   .  95   .   .   .   .   .   .   . 
 15 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 16 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 17 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 18 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 19 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 20 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 26 112   .  20   .   .   .   .   .   .   .  76   .   .   .   .   .   .   .  16 
 28 112   . 100   .   .   .   .   .   .   .   .   .   .   .   .   .  12   .   . 
 30 112   .  24   .   .   .   .   .   .   .   .   .   .   .   .   .  12   .  76 
 37 112   . 100   .   .   .   .   .  12   .   .   .   .   .   .   .   .   .   . 
 38 112   .  29  83   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 45 112   .  96   .   .  16   .   .   .   .   .   .   .   .   .   .   .   .   . 
 46 112   . 100   .   .  12   .   .   .   .   .   .   .   .   .   .   .   .   . 
 47 112   . 100   .   .   .  12   .   .   .   .   .   .   .   .   .   .   .   . 
 52 112   . 100   .   .   .   .   .   .   .  12   .   .   .   .   .   .   .   . 
 53 112   .  54   .   .   .   .   .   .   .  58   .   .   .   .   .   .   .   . 
 55 112   .  57   .   .   .   .   .   .   .  12   .   .  43   .   .   .   .   . 
 56 112   . 109   .   .   .   .   .   .   .   3   .   .   .   .   .   .   .   . 
 57 112   .  14  33  64   .   .   .   .   .   .   .   .   .   .   .   1   .   . 
 66 112   .  97   .  15   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 67 112   .  97   .   .   .   .   .  15   .   .   .   .   .   .   .   .   .   . 
 70 112   3  50   .   .   3   .   .   .   .   .   .   .   .   .  56   .   .   . 
 71 112   3  14   .   3   .   .   .   .  12   .   .   .   .   .   .   .  80   . 
 72 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 73 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 74 112   3  17  12   .  80   .   .   .   .   .   .   .   .   .   .   .   .   . 
 75 112   3  29   .   .   .   .   .   .   .  80   .   .   .   .   .   .   .   . 
 76 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 77 112   3  26   .   .   .   .   .   .   .   .   .   .   .   .   .   .  83   . 
 78 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 79 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 80 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 81 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 82 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 83 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 84 112   3  51   .   .   .   .   .   .   .   .   .   .   .  58   .   .   .   . 
 85 112   3  51   .   .   .   .   .   .   .  58   .   .   .   .   .   .   .   . 
 86 112   3  50   .   .  58   .   1   .   .   .   .   .   .   .   .   .   .   . 
 87 112   3  15   .   .   .  36   .   .   .  58   .   .   .   .   .   .   .   . 
 88 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 89 112   3  51   .   .   .   .   .   .   .   .   .   .   .   .   .   .  58   . 
 90 112   3  51   .   .   .   .   .   .   .   .   .   .   .   .   .   .  58   . 
 91 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 92 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 93 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 94 112  17  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 95 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num   *   -   A   D   E   F   G   I   K   L   M   N   P   Q   R   S   T   Y 
-31 120 112   .   .   .   .   .   .   .   .   .   .   8   .   .   .   .   .   . 
-30 120 112   .   8   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-29 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-28 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-27 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-26 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-25 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-24 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-23 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-22 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-21 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-20 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-19 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-18 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-17 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-16 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-15 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-14 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-13 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-12 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-11 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-10 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -9 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -8 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -7 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -6 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -5 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -4 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -3 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -2 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -1 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  . 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  1 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  2 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  3 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  4 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  5 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  6 112  20  92   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  7 112  20  92   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  8 112  20  92   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  9 112   3  76   .   .   .  33   .   .   .   .   .   .   .   .   .   .   .   . 
 10 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 11 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 12 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 13 112   3  93  16   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 14 112   3  14   .   .   .   .   .   .   .   .  95   .   .   .   .   .   .   . 
 15 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 16 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 17 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 18 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 19 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 20 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 26 112   .  20   .   .   .   .   .   .   .  76   .   .   .   .   .   .   .  16 
 28 112   . 100   .   .   .   .   .   .   .   .   .   .   .   .   .  12   .   . 
 30 112   .  24   .   .   .   .   .   .   .   .   .   .   .   .   .  12   .  76 
 37 112   . 100   .   .   .   .   .  12   .   .   .   .   .   .   .   .   .   . 
 38 112   .  29  83   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 45 112   .  96   .   .  16   .   .   .   .   .   .   .   .   .   .   .   .   . 
 46 112   . 100   .   .  12   .   .   .   .   .   .   .   .   .   .   .   .   . 
 47 112   . 100   .   .   .  12   .   .   .   .   .   .   .   .   .   .   .   . 
 52 112   . 100   .   .   .   .   .   .   .  12   .   .   .   .   .   .   .   . 
 53 112   .  54   .   .   .   .   .   .   .  58   .   .   .   .   .   .   .   . 
 55 112   .  57   .   .   .   .   .   .   .  12   .   .  43   .   .   .   .   . 
 56 112   . 109   .   .   .   .   .   .   .   3   .   .   .   .   .   .   .   . 
 57 112   .  14  33  64   .   .   .   .   .   .   .   .   .   .   .   1   .   . 
 66 112   .  97   .  15   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 67 112   .  97   .   .   .   .   .  15   .   .   .   .   .   .   .   .   .   . 
 70 112   3  50   .   .   3   .   .   .   .   .   .   .   .   .  56   .   .   . 
 71 112   3  14   .   3   .   .   .   .  12   .   .   .   .   .   .   .  80   . 
 72 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 73 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 74 112   3  17  12   .  80   .   .   .   .   .   .   .   .   .   .   .   .   . 
 75 112   3  29   .   .   .   .   .   .   .  80   .   .   .   .   .   .   .   . 
 76 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 77 112   3  26   .   .   .   .   .   .   .   .   .   .   .   .   .   .  83   . 
 78 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 79 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 80 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 81 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 82 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 83 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 84 112   3  51   .   .   .   .   .   .   .   .   .   .   .  58   .   .   .   . 
 85 112   3  51   .   .   .   .   .   .   .  58   .   .   .   .   .   .   .   . 
 86 112   3  50   .   .  58   .   1   .   .   .   .   .   .   .   .   .   .   . 
 87 112   3  15   .   .   .  36   .   .   .  58   .   .   .   .   .   .   .   . 
 88 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 89 112   3  51   .   .   .   .   .   .   .   .   .   .   .   .   .   .  58   . 
 90 112   3  51   .   .   .   .   .   .   .   .   .   .   .   .   .   .  58   . 
 91 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 92 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 93 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 94 112  17  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 95 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 96 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 97 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 98 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 99 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
100 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
101 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
102 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
103 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
104 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
105 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
106 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
107 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
108 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
109 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
110 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
111 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
112 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
113 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
114 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
115 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
116 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
117 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
118 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
119 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
120 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
121 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
122 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
123 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
124 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
125 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
126 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
127 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
128 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
129 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
130 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
131 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
132 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
133 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
134 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
135 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
136 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
137 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
138 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
139 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
140 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
141 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
142 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
143 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
144 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
145 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
146 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
147 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
148 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
149 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
150 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
151 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
152 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
153 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
154 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
155 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
156 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
157 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
158 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
159 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
160 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
161 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
162 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
163 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
164 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
165 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
166 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
167 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
168 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
169 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
170 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
171 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
172 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
173 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
174 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
175 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
176 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
177 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
178 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
179 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
180 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
181 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
182 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
183 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
184 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
185 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
186 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
187 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
188 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
189 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
190 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
191 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
192 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
193 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
194 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
195 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
196 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
197 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
198 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
199 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
200 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
201 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
202 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
203 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
204 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
205 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
206 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
207 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
208 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
209 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
210 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
211 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
212 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
213 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
214 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
215 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
216 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
217 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
218 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
219 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
220 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
221 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
222 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
223 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
224 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
225 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
226 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
227 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
228 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
229 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
230 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
231 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
232 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
233 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
234 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
235 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
236 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
237 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
SNP genotypes: 
    60 samples X 275 SNPs
    SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
    min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
    min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C 
125  97  32  21 
Exclude 9 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:35
[1] 2019-04-13 03:03:35, OOB Acc: 86.96%, # of SNPs: 11, # of Haplo: 48
[2] 2019-04-13 03:03:35, OOB Acc: 82.61%, # of SNPs: 14, # of Haplo: 54
[3] 2019-04-13 03:03:36, OOB Acc: 97.22%, # of SNPs: 21, # of Haplo: 50
[4] 2019-04-13 03:03:36, OOB Acc: 90.38%, # of SNPs: 16, # of Haplo: 34
[5] 2019-04-13 03:03:36, OOB Acc: 95.45%, # of SNPs: 20, # of Haplo: 38
[6] 2019-04-13 03:03:36, OOB Acc: 95.83%, # of SNPs: 18, # of Haplo: 83
[7] 2019-04-13 03:03:37, OOB Acc: 100.00%, # of SNPs: 24, # of Haplo: 100
[8] 2019-04-13 03:03:37, OOB Acc: 100.00%, # of SNPs: 18, # of Haplo: 48
[9] 2019-04-13 03:03:37, OOB Acc: 94.74%, # of SNPs: 15, # of Haplo: 28
[10] 2019-04-13 03:03:38, OOB Acc: 83.33%, # of SNPs: 12, # of Haplo: 20
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002170206 0.0002218348 0.0002651623 0.0035806631 0.0108171268 0.0260212634 
        Max.         Mean           SD 
0.4900436399 0.0436176972 0.1079908676 
Accuracy with training data: 97.5%
Out-of-bag accuracy: 92.7%
Gene: A
Training dataset: 60 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 10
    total # of SNPs used: 104
    avg. # of SNPs in an individual classifier: 16.90
        (sd: 4.09, min: 11, max: 24, median: 17.00)
    avg. # of haplotypes in an individual classifier: 50.30
        (sd: 24.49, min: 20, max: 100, median: 48.00)
    avg. out-of-bag accuracy: 92.65%
        (sd: 6.47%, min: 82.61%, max: 100.00%, median: 95.10%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002170206 0.0002218348 0.0002651623 0.0035806631 0.0108171268 0.0260212634 
        Max.         Mean           SD 
0.4900436399 0.0436176972 0.1079908676 
Genome assembly: hg19
SNP genotypes: 
    60 samples X 275 SNPs
    SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
    min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
    min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C 
125  97  32  21 
SNP genotypes: 
    60 samples X 275 SNPs
    SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
    min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
    min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C 
125  97  32  21 
Loading required namespace: gdsfmt
Loading required namespace: SNPRelate
Import 2348 SNPs from chromosome 6.
3 SNPs with invalid alleles have been removed.
SNP genotypes: 
    165 samples X 2345 SNPs
    SNPs range from 24652946bp to 33524089bp on hg18
Missing rate per SNP:
    min: 0, max: 0.0484848, mean: 0.001698, median: 0, sd: 0.00504919
Missing rate per sample:
    min: 0, max: 0.0110874, mean: 0.001698, median: 0.000852878, sd: 0.00195105
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.194167, median: 0.172727, sd: 0.149319
Allelic information:
A/G T/C G/A C/T T/G A/C C/A G/T C/G G/C A/T T/A 
556 467 410 400 116 109 103  81  30  29  28  16 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
No allelic strand orders are switched.
SNP genotypes: 
    150 samples X 1214 SNPs
    SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0866667, mean: 0.0844646, median: 0.0866667, sd: 0.0128841
Missing rate per sample:
    min: 0, max: 0.968699, mean: 0.0844646, median: 0.000823723, sd: 0.273119
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.234168, median: 0.218978, sd: 0.137855
Allelic information:
A/G C/T G/T A/C 
505 496 109 104 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
SNP genotypes: 
    60 samples X 1197 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0657059, median: 0.0666667, sd: 0.00757446
Missing rate per sample:
    min: 0, max: 0.978279, mean: 0.0657059, median: 0.000835422, sd: 0.245786
Minor allele frequency:
    min: 0.101695, max: 0.5, mean: 0.278734, median: 0.267857, sd: 0.117338
Allelic information:
A/G C/T A/C G/T 
511 476 105 105 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
SNP genotypes: 
    90 samples X 3932 SNPs
    SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
    min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
    min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
 A/G  C/T  G/T  A/C  C/G  A/T 
1567 1510  348  332  111   64 
No allelic strand orders are switched.
SNP genotypes: 
    60 samples X 1214 SNPs
    SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0650879, median: 0.0666667, sd: 0.0097381
Missing rate per sample:
    min: 0, max: 0.968699, mean: 0.0650879, median: 0.000823723, sd: 0.243373
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.234476, median: 0.223214, sd: 0.13833
Allelic information:
A/G C/T G/T A/C 
505 496 109 104 
SNP genotypes: 
    60 samples X 275 SNPs
    SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
    min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
    min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C 
125  97  32  21 
MAF filter (>=0.01), excluding 9 SNP(s)
using the default genome assembly (assembly="hg19")
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:40
[1] 2019-04-13 03:03:40, OOB Acc: 92.00%, # of SNPs: 24, # of Haplo: 29
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.222247e-28 1.128571e-24 1.128371e-23 6.944660e-04 8.333349e-03 3.673611e-02 
        Max.         Mean           SD 
9.105734e-02 2.054649e-02 2.598603e-02 
Accuracy with training data: 96.7%
Out-of-bag accuracy: 92.0%
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:40
[1] 2019-04-13 03:03:40, OOB Acc: 97.50%, # of SNPs: 18, # of Haplo: 34
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
5.014366e-13 4.671716e-10 4.667203e-09 1.640727e-03 7.504546e-03 2.126745e-02 
        Max.         Mean           SD 
9.784316e-02 1.490504e-02 1.947399e-02 
Accuracy with training data: 97.5%
Out-of-bag accuracy: 97.5%
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:40
[1] 2019-04-13 03:03:40, OOB Acc: 88.89%, # of SNPs: 14, # of Haplo: 38
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.222223e-18 6.603163e-16 6.583163e-15 1.944468e-03 1.020834e-02 4.122739e-02 
        Max.         Mean           SD 
1.808372e-01 2.422083e-02 3.699146e-02 
Accuracy with training data: 95.8%
Out-of-bag accuracy: 88.9%
Gene: C
Training dataset: 60 samples X 83 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 3
    total # of SNPs used: 40
    avg. # of SNPs in an individual classifier: 18.67
        (sd: 5.03, min: 14, max: 24, median: 18.00)
    avg. # of haplotypes in an individual classifier: 33.67
        (sd: 4.51, min: 29, max: 38, median: 34.00)
    avg. out-of-bag accuracy: 92.80%
        (sd: 4.36%, min: 88.89%, max: 97.50%, median: 92.00%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
1.708707e-13 1.229313e-05 1.229313e-04 1.860746e-03 9.050936e-03 3.332722e-02 
        Max.         Mean           SD 
1.210500e-01 1.989079e-02 2.507466e-02 
Genome assembly: hg19
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77, # of samples: 60
# of unique HLA alleles: 12
[-] 2019-04-13 03:03:40
[1] 2019-04-13 03:03:40, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20
[2] 2019-04-13 03:03:40, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 
        Max.         Mean           SD 
4.728168e-01 4.409445e-02 1.068815e-01 
Accuracy with training data: 95.0%
Out-of-bag accuracy: 94.5%
Gene: DQB1
Training dataset: 60 samples X 77 SNPs
    # of HLA alleles: 12
    # of individual classifiers: 2
    total # of SNPs used: 20
    avg. # of SNPs in an individual classifier: 14.00
        (sd: 1.41, min: 13, max: 15, median: 14.00)
    avg. # of haplotypes in an individual classifier: 20.50
        (sd: 0.71, min: 20, max: 21, median: 20.50)
    avg. out-of-bag accuracy: 94.45%
        (sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 
        Max.         Mean           SD 
4.728168e-01 4.409445e-02 1.068815e-01 
Genome assembly: hg19
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:40
[1] 2019-04-13 03:03:40, OOB Acc: 86.96%, # of SNPs: 11, # of Haplo: 48
[2] 2019-04-13 03:03:41, OOB Acc: 82.61%, # of SNPs: 14, # of Haplo: 54
[3] 2019-04-13 03:03:41, OOB Acc: 97.22%, # of SNPs: 21, # of Haplo: 50
[4] 2019-04-13 03:03:41, OOB Acc: 90.38%, # of SNPs: 16, # of Haplo: 34
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0001562613 0.0001654796 0.0002484448 0.0031072694 0.0084676824 0.0267061654 
        Max.         Mean           SD 
0.5667222637 0.0472474454 0.1286381649 
Accuracy with training data: 98.3%
Out-of-bag accuracy: 89.3%
Gene: A
Training dataset: 60 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 50
    avg. # of SNPs in an individual classifier: 15.50
        (sd: 4.20, min: 11, max: 21, median: 15.00)
    avg. # of haplotypes in an individual classifier: 46.50
        (sd: 8.70, min: 34, max: 54, median: 49.00)
    avg. out-of-bag accuracy: 89.29%
        (sd: 6.17%, min: 82.61%, max: 97.22%, median: 88.67%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0001562613 0.0001654796 0.0002484448 0.0031072694 0.0084676824 0.0267061654 
        Max.         Mean           SD 
0.5667222637 0.0472474454 0.1286381649 
Genome assembly: hg19
Gene: A
Training dataset: 60 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 50
    avg. # of SNPs in an individual classifier: 15.50
        (sd: 4.20, min: 11, max: 21, median: 15.00)
    avg. # of haplotypes in an individual classifier: 46.50
        (sd: 8.70, min: 34, max: 54, median: 49.00)
    avg. out-of-bag accuracy: 89.29%
        (sd: 6.17%, min: 82.61%, max: 97.22%, median: 88.67%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0001562613 0.0001654796 0.0002484448 0.0031072694 0.0084676824 0.0267061654 
        Max.         Mean           SD 
0.5667222637 0.0472474454 0.1286381649 
Genome assembly: hg19
Sat Apr 13 03:03:41 2019, passing the 1/4 classifiers.
Sat Apr 13 03:03:41 2019, passing the 2/4 classifiers.
Sat Apr 13 03:03:41 2019, passing the 3/4 classifiers.
Sat Apr 13 03:03:41 2019, passing the 4/4 classifiers.
Allele	Num.	Freq.	CR	ACC	SEN	SPE	PPV	NPV	Miscall
	Valid.	Valid.	(%)	(%)	(%)	(%)	(%)	(%)	(%)
----
Overall accuracy: 88.1%, Call rate: 100.0%
01:01 25 0.2083 100.0 99.5 100.0 99.4 95.8 100.0 --
02:01 43 0.3583 100.0 96.1 100.0 94.2 89.0 100.0 --
02:06 1 0.0083 50.0 97.6 0.0 100.0 -- 97.6 02:01 (100)
03:01 9 0.0750 100.0 96.2 93.8 96.4 72.5 99.4 24:02 (100)
11:01 5 0.0417 100.0 100.0 100.0 100.0 100.0 100.0 --
23:01 3 0.0250 75.0 99.4 83.3 100.0 100.0 99.3 24:02 (100)
24:02 11 0.0917 100.0 98.3 91.7 98.9 90.8 99.3 03:01 (100)
24:03 1 0.0083 50.0 97.8 0.0 100.0 -- 97.8 01:01 (50)
25:01 5 0.0417 100.0 98.4 100.0 98.3 84.7 100.0 --
26:01 3 0.0250 100.0 97.8 50.0 100.0 100.0 97.8 25:01 (62)
29:02 4 0.0333 75.0 93.6 33.3 99.3 50.0 94.2 02:01 (50)
31:01 3 0.0250 75.0 100.0 100.0 100.0 100.0 100.0 --
32:01 4 0.0333 50.0 100.0 100.0 100.0 100.0 100.0 --
68:01 3 0.0250 100.0 98.6 75.0 100.0 100.0 98.6 03:01 (67)
\title{Imputation Evaluation}

\documentclass[12pt]{article}

\usepackage{fullpage}
\usepackage{longtable}

\begin{document}

\maketitle

\setlength{\LTcapwidth}{6.5in}

% -------- BEGIN TABLE --------
\begin{longtable}{rrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
 & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{10}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
 & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{10}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{10}{l}{\it Overall accuracy: 88.1\%, Call rate: 100.0\%} \\
01:01 & 25 & 0.2083 & 100.0 & 99.5 & 100.0 & 99.4 & 95.8 & 100.0 & -- \\
02:01 & 43 & 0.3583 & 100.0 & 96.1 & 100.0 & 94.2 & 89.0 & 100.0 & -- \\
02:06 & 1 & 0.0083 & 50.0 & 97.6 & 0.0 & 100.0 & -- & 97.6 & 02:01 (100) \\
03:01 & 9 & 0.0750 & 100.0 & 96.2 & 93.8 & 96.4 & 72.5 & 99.4 & 24:02 (100) \\
11:01 & 5 & 0.0417 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
23:01 & 3 & 0.0250 & 75.0 & 99.4 & 83.3 & 100.0 & 100.0 & 99.3 & 24:02 (100) \\
24:02 & 11 & 0.0917 & 100.0 & 98.3 & 91.7 & 98.9 & 90.8 & 99.3 & 03:01 (100) \\
24:03 & 1 & 0.0083 & 50.0 & 97.8 & 0.0 & 100.0 & -- & 97.8 & 01:01 (50) \\
25:01 & 5 & 0.0417 & 100.0 & 98.4 & 100.0 & 98.3 & 84.7 & 100.0 & -- \\
26:01 & 3 & 0.0250 & 100.0 & 97.8 & 50.0 & 100.0 & 100.0 & 97.8 & 25:01 (62) \\
29:02 & 4 & 0.0333 & 75.0 & 93.6 & 33.3 & 99.3 & 50.0 & 94.2 & 02:01 (50) \\
31:01 & 3 & 0.0250 & 75.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
32:01 & 4 & 0.0333 & 50.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 3 & 0.0250 & 100.0 & 98.6 & 75.0 & 100.0 & 100.0 & 98.6 & 03:01 (67) \\
\end{longtable}
% -------- END TABLE --------

\end{document}
<!DOCTYPE html>
<html>
<head>
  <title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1"  CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="10">
<i> Overall accuracy: 88.1%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>25</td> <td>0.2083</td> <td>100.0</td> <td>99.5</td> <td>100.0</td> <td>99.4</td> <td>95.8</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>43</td> <td>0.3583</td> <td>100.0</td> <td>96.1</td> <td>100.0</td> <td>94.2</td> <td>89.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0083</td> <td>50.0</td> <td>97.6</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.6</td> <td>02:01 (100)</td>
</tr>
<tr>
<td>03:01</td> <td>9</td> <td>0.0750</td> <td>100.0</td> <td>96.2</td> <td>93.8</td> <td>96.4</td> <td>72.5</td> <td>99.4</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>11:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>3</td> <td>0.0250</td> <td>75.0</td> <td>99.4</td> <td>83.3</td> <td>100.0</td> <td>100.0</td> <td>99.3</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>24:02</td> <td>11</td> <td>0.0917</td> <td>100.0</td> <td>98.3</td> <td>91.7</td> <td>98.9</td> <td>90.8</td> <td>99.3</td> <td>03:01 (100)</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0083</td> <td>50.0</td> <td>97.8</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.8</td> <td>01:01 (50)</td>
</tr>
<tr>
<td>25:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>98.4</td> <td>100.0</td> <td>98.3</td> <td>84.7</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>26:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>97.8</td> <td>50.0</td> <td>100.0</td> <td>100.0</td> <td>97.8</td> <td>25:01 (62)</td>
</tr>
<tr>
<td>29:02</td> <td>4</td> <td>0.0333</td> <td>75.0</td> <td>93.6</td> <td>33.3</td> <td>99.3</td> <td>50.0</td> <td>94.2</td> <td>02:01 (50)</td>
</tr>
<tr>
<td>31:01</td> <td>3</td> <td>0.0250</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>32:01</td> <td>4</td> <td>0.0333</td> <td>50.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>98.6</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>98.6</td> <td>03:01 (67)</td>
</tr>
</table>

</body>
</html>
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes:
2019-04-13 03:03:42,   1, job  1, # of SNPs: 15, # of haplo: 29, acc: 68.2%
  --  avg out-of-bag acc: 68.18%, sd: NA%, min: 68.18%, max: 68.18%
2019-04-13 03:03:42,   2, job  1, # of SNPs: 11, # of haplo: 20, acc: 75.0%
  --  avg out-of-bag acc: 71.59%, sd: 4.82%, min: 68.18%, max: 75.00%
2019-04-13 03:03:42,   3, job  2, # of SNPs: 13, # of haplo: 25, acc: 77.3%
  --  avg out-of-bag acc: 73.48%, sd: 4.73%, min: 68.18%, max: 77.27%
2019-04-13 03:03:42,   4, job  1, # of SNPs: 12, # of haplo: 23, acc: 89.3%
  --  avg out-of-bag acc: 77.44%, sd: 8.79%, min: 68.18%, max: 89.29%
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0006488053 0.0006523782 0.0006845350 0.0050013866 0.0110310747 0.0298617344 
        Max.         Mean           SD 
0.3392277674 0.0432960091 0.0837228980 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 77.4%
Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes:
The model is autosaved in 'tmp_model.RData'.
2019-04-13 03:03:43,   1, job  2, # of SNPs: 15, # of haplo: 28, acc: 79.2%
  --  avg out-of-bag acc: 79.17%, sd: NA%, min: 79.17%, max: 79.17%
2019-04-13 03:03:43,   2, job  2, # of SNPs: 12, # of haplo: 40, acc: 87.5%
  --  avg out-of-bag acc: 83.33%, sd: 5.89%, min: 79.17%, max: 87.50%
2019-04-13 03:03:43,   3, job  1, # of SNPs: 13, # of haplo: 46, acc: 87.5%
Stop "job 1".
  --  avg out-of-bag acc: 84.72%, sd: 4.81%, min: 79.17%, max: 87.50%
2019-04-13 03:03:43,   4, job  1, # of SNPs: 11, # of haplo: 19, acc: 86.4%
Stop "job 1".
  --  avg out-of-bag acc: 85.13%, sd: 4.01%, min: 79.17%, max: 87.50%
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004325303 0.0004325306 0.0004325332 0.0045836653 0.0086680504 0.0199862786 
        Max.         Mean           SD 
0.4708241730 0.0410341661 0.1073727492 
Accuracy with training data: 94.1%
Out-of-bag accuracy: 85.1%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 41
    avg. # of SNPs in an individual classifier: 12.75
        (sd: 1.71, min: 11, max: 15, median: 12.50)
    avg. # of haplotypes in an individual classifier: 33.25
        (sd: 12.09, min: 19, max: 46, median: 34.00)
    avg. out-of-bag accuracy: 85.13%
        (sd: 4.01%, min: 79.17%, max: 87.50%, median: 86.93%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004325303 0.0004325306 0.0004325332 0.0045836653 0.0086680504 0.0199862786 
        Max.         Mean           SD 
0.4708241730 0.0410341661 0.1073727492 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:43)	0%
Predicting (2019-04-13 03:03:43)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 15
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.8%)  4 (15.4%)  5 (19.2%) 16 (61.5%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000001 0.0018773 0.0062051 0.0290716 0.0230480 0.4708242 
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Run in parallel with 2 compute nodes.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 11
# of unique HLA genotypes: 15
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.8%)  4 (15.4%)  5 (19.2%) 16 (61.5%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000001 0.0018773 0.0062051 0.0290716 0.0230480 0.4708242 
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:43
     1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
     2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
     3, SNP: 204, Loss: 130.453, OOB Acc: 72.73%, # of Haplo: 16
     4, SNP: 87, Loss: 81.1116, OOB Acc: 77.27%, # of Haplo: 20
     5, SNP: 115, Loss: 60.8965, OOB Acc: 86.36%, # of Haplo: 21
     6, SNP: 191, Loss: 35.3274, OOB Acc: 90.91%, # of Haplo: 22
     7, SNP: 220, Loss: 23.2514, OOB Acc: 90.91%, # of Haplo: 24
     8, SNP: 47, Loss: 19.9968, OOB Acc: 90.91%, # of Haplo: 25
     9, SNP: 20, Loss: 18.0597, OOB Acc: 90.91%, # of Haplo: 27
    10, SNP: 171, Loss: 17.3174, OOB Acc: 90.91%, # of Haplo: 27
    11, SNP: 158, Loss: 16.9514, OOB Acc: 90.91%, # of Haplo: 29
    12, SNP: 247, Loss: 15.5658, OOB Acc: 90.91%, # of Haplo: 35
    13, SNP: 193, Loss: 15.4994, OOB Acc: 90.91%, # of Haplo: 44
    14, SNP: 73, Loss: 12.4986, OOB Acc: 90.91%, # of Haplo: 45
    15, SNP: 199, Loss: 11.7779, OOB Acc: 90.91%, # of Haplo: 45
    16, SNP: 151, Loss: 11.0487, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:03:43, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 45
     1, SNP: 171, Loss: 169.639, OOB Acc: 56.67%, # of Haplo: 14
     2, SNP: 127, Loss: 146.326, OOB Acc: 63.33%, # of Haplo: 16
     3, SNP: 109, Loss: 104.178, OOB Acc: 66.67%, # of Haplo: 18
     4, SNP: 107, Loss: 90.6768, OOB Acc: 73.33%, # of Haplo: 20
     5, SNP: 163, Loss: 66.9122, OOB Acc: 76.67%, # of Haplo: 25
     6, SNP: 185, Loss: 60.1057, OOB Acc: 76.67%, # of Haplo: 33
     7, SNP: 99, Loss: 55.2312, OOB Acc: 76.67%, # of Haplo: 39
     8, SNP: 199, Loss: 47.686, OOB Acc: 76.67%, # of Haplo: 39
     9, SNP: 95, Loss: 46.9138, OOB Acc: 80.00%, # of Haplo: 47
    10, SNP: 70, Loss: 45.9845, OOB Acc: 80.00%, # of Haplo: 49
    11, SNP: 129, Loss: 45.86, OOB Acc: 83.33%, # of Haplo: 49
[2] 2019-04-13 03:03:43, OOB Acc: 83.33%, # of SNPs: 11, # of Haplo: 49
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.677486e-08 3.642759e-06 3.573661e-05 1.908829e-03 6.558496e-03 2.050541e-02 
        Max.         Mean           SD 
5.935371e-01 5.215016e-02 1.400728e-01 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 87.1%
Exclude 9 monomorphic SNPs
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:43
     1, SNP: 122, Loss: 162.121, OOB Acc: 50.00%, # of Haplo: 12
     2, SNP: 137, Loss: 155.367, OOB Acc: 58.33%, # of Haplo: 12
     3, SNP: 81, Loss: 120.826, OOB Acc: 58.33%, # of Haplo: 16
     4, SNP: 146, Loss: 83.2015, OOB Acc: 62.50%, # of Haplo: 21
     5, SNP: 148, Loss: 68.8368, OOB Acc: 62.50%, # of Haplo: 21
     6, SNP: 51, Loss: 40.3043, OOB Acc: 62.50%, # of Haplo: 21
     7, SNP: 231, Loss: 29.1968, OOB Acc: 62.50%, # of Haplo: 23
     8, SNP: 87, Loss: 23.3916, OOB Acc: 62.50%, # of Haplo: 31
     9, SNP: 160, Loss: 20.422, OOB Acc: 62.50%, # of Haplo: 31
    10, SNP: 119, Loss: 20.2055, OOB Acc: 62.50%, # of Haplo: 32
    11, SNP: 9, Loss: 19.9994, OOB Acc: 62.50%, # of Haplo: 37
    12, SNP: 199, Loss: 19.2624, OOB Acc: 62.50%, # of Haplo: 38
    13, SNP: 136, Loss: 19.0415, OOB Acc: 62.50%, # of Haplo: 40
    14, SNP: 151, Loss: 18.6147, OOB Acc: 62.50%, # of Haplo: 40
    15, SNP: 3, Loss: 18.5586, OOB Acc: 62.50%, # of Haplo: 45
    16, SNP: 73, Loss: 14.2585, OOB Acc: 62.50%, # of Haplo: 46
[1] 2019-04-13 03:03:44, OOB Acc: 62.50%, # of SNPs: 16, # of Haplo: 46
     1, SNP: 122, Loss: 185.298, OOB Acc: 75.00%, # of Haplo: 13
     2, SNP: 118, Loss: 178.626, OOB Acc: 83.33%, # of Haplo: 13
     3, SNP: 131, Loss: 149.969, OOB Acc: 87.50%, # of Haplo: 14
     4, SNP: 144, Loss: 97.8546, OOB Acc: 87.50%, # of Haplo: 14
     5, SNP: 123, Loss: 62.1051, OOB Acc: 91.67%, # of Haplo: 14
     6, SNP: 105, Loss: 48.1095, OOB Acc: 95.83%, # of Haplo: 14
     7, SNP: 197, Loss: 30.2151, OOB Acc: 95.83%, # of Haplo: 16
     8, SNP: 40, Loss: 28.3663, OOB Acc: 95.83%, # of Haplo: 20
     9, SNP: 90, Loss: 24.3188, OOB Acc: 95.83%, # of Haplo: 22
    10, SNP: 263, Loss: 19.7509, OOB Acc: 95.83%, # of Haplo: 31
    11, SNP: 67, Loss: 15.2521, OOB Acc: 95.83%, # of Haplo: 31
    12, SNP: 199, Loss: 14.3355, OOB Acc: 95.83%, # of Haplo: 33
    13, SNP: 73, Loss: 9.42737, OOB Acc: 95.83%, # of Haplo: 33
[2] 2019-04-13 03:03:44, OOB Acc: 95.83%, # of SNPs: 13, # of Haplo: 33
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
6.055448e-13 1.784206e-06 1.784206e-05 1.513906e-03 5.745114e-03 1.789755e-02 
        Max.         Mean           SD 
3.732254e-01 3.556249e-02 8.857257e-02 
Accuracy with training data: 94.1%
Out-of-bag accuracy: 79.2%
HIBAG model: 2 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting by voting from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:44)	0%
Predicting (2019-04-13 03:03:44)	100%
HIBAG model: 2 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting by voting from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:44)	0%
Predicting (2019-04-13 03:03:44)	100%
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 13
# of SNPs: 158, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:44
     1, SNP: 141, Loss: 378.06, OOB Acc: 52.08%, # of Haplo: 14
     2, SNP: 74, Loss: 262.497, OOB Acc: 58.33%, # of Haplo: 15
     3, SNP: 78, Loss: 162.497, OOB Acc: 68.75%, # of Haplo: 19
     4, SNP: 118, Loss: 70.0426, OOB Acc: 72.92%, # of Haplo: 23
     5, SNP: 82, Loss: 45.8279, OOB Acc: 83.33%, # of Haplo: 23
     6, SNP: 95, Loss: 35.4414, OOB Acc: 89.58%, # of Haplo: 27
     7, SNP: 89, Loss: 32.6134, OOB Acc: 89.58%, # of Haplo: 35
     8, SNP: 83, Loss: 31.7921, OOB Acc: 89.58%, # of Haplo: 51
     9, SNP: 151, Loss: 31.0653, OOB Acc: 89.58%, # of Haplo: 55
    10, SNP: 94, Loss: 31.0246, OOB Acc: 89.58%, # of Haplo: 55
    11, SNP: 111, Loss: 18.9027, OOB Acc: 89.58%, # of Haplo: 56
    12, SNP: 139, Loss: 18.4248, OOB Acc: 89.58%, # of Haplo: 59
    13, SNP: 93, Loss: 17.0195, OOB Acc: 91.67%, # of Haplo: 58
    14, SNP: 15, Loss: 14.1692, OOB Acc: 91.67%, # of Haplo: 60
[1] 2019-04-13 03:03:44, OOB Acc: 91.67%, # of SNPs: 14, # of Haplo: 60
     1, SNP: 94, Loss: 403.365, OOB Acc: 63.16%, # of Haplo: 15
     2, SNP: 82, Loss: 294.053, OOB Acc: 71.05%, # of Haplo: 18
     3, SNP: 57, Loss: 226.142, OOB Acc: 76.32%, # of Haplo: 23
     4, SNP: 155, Loss: 197.199, OOB Acc: 84.21%, # of Haplo: 29
     5, SNP: 44, Loss: 132.804, OOB Acc: 86.84%, # of Haplo: 40
     6, SNP: 30, Loss: 122.507, OOB Acc: 92.11%, # of Haplo: 40
     7, SNP: 109, Loss: 72.0179, OOB Acc: 92.11%, # of Haplo: 41
     8, SNP: 72, Loss: 59.3281, OOB Acc: 92.11%, # of Haplo: 41
     9, SNP: 36, Loss: 54.939, OOB Acc: 94.74%, # of Haplo: 43
    10, SNP: 127, Loss: 48.1392, OOB Acc: 94.74%, # of Haplo: 43
    11, SNP: 53, Loss: 44.7676, OOB Acc: 94.74%, # of Haplo: 43
    12, SNP: 148, Loss: 43.047, OOB Acc: 94.74%, # of Haplo: 44
    13, SNP: 152, Loss: 40.2104, OOB Acc: 94.74%, # of Haplo: 45
    14, SNP: 125, Loss: 39.8862, OOB Acc: 94.74%, # of Haplo: 45
    15, SNP: 78, Loss: 39.5652, OOB Acc: 94.74%, # of Haplo: 45
    16, SNP: 3, Loss: 39.0621, OOB Acc: 94.74%, # of Haplo: 47
    17, SNP: 141, Loss: 37.6822, OOB Acc: 94.74%, # of Haplo: 47
    18, SNP: 1, Loss: 36.5022, OOB Acc: 94.74%, # of Haplo: 50
[2] 2019-04-13 03:03:44, OOB Acc: 94.74%, # of SNPs: 18, # of Haplo: 50
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 
        Max.         Mean           SD 
5.948644e-01 6.324378e-02 1.404915e-01 
Accuracy with training data: 96.7%
Out-of-bag accuracy: 93.2%
Gene: A
Training dataset: 60 samples X 158 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 2
    total # of SNPs used: 28
    avg. # of SNPs in an individual classifier: 16.00
        (sd: 2.83, min: 14, max: 18, median: 16.00)
    avg. # of haplotypes in an individual classifier: 55.00
        (sd: 7.07, min: 50, max: 60, median: 55.00)
    avg. out-of-bag accuracy: 93.20%
        (sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 
        Max.         Mean           SD 
5.948644e-01 6.324378e-02 1.404915e-01 
Genome assembly: hg19
Remove 130 unused SNPs.
Gene: A
Training dataset: 60 samples X 28 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 2
    total # of SNPs used: 28
    avg. # of SNPs in an individual classifier: 16.00
        (sd: 2.83, min: 14, max: 18, median: 16.00)
    avg. # of haplotypes in an individual classifier: 55.00
        (sd: 7.07, min: 50, max: 60, median: 55.00)
    avg. out-of-bag accuracy: 93.20%
        (sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 
        Max.         Mean           SD 
5.948644e-01 6.324378e-02 1.404915e-01 
Genome assembly: hg19
Platform: Illumina 1M Duo 
Information: Training set -- HapMap Phase II 
HIBAG model: 2 individual classifiers, 158 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 60
Predicting (2019-04-13 03:03:44)	0%
Predicting (2019-04-13 03:03:44)	100%
HIBAG model: 2 individual classifiers, 28 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 60
Predicting (2019-04-13 03:03:44)	0%
Predicting (2019-04-13 03:03:44)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:44
     1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
     2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
     3, SNP: 204, Loss: 130.453, OOB Acc: 72.73%, # of Haplo: 16
     4, SNP: 87, Loss: 81.1116, OOB Acc: 77.27%, # of Haplo: 20
     5, SNP: 115, Loss: 60.8965, OOB Acc: 86.36%, # of Haplo: 21
     6, SNP: 191, Loss: 35.3274, OOB Acc: 90.91%, # of Haplo: 22
     7, SNP: 220, Loss: 23.2514, OOB Acc: 90.91%, # of Haplo: 24
     8, SNP: 47, Loss: 19.9968, OOB Acc: 90.91%, # of Haplo: 25
     9, SNP: 20, Loss: 18.0597, OOB Acc: 90.91%, # of Haplo: 27
    10, SNP: 171, Loss: 17.3174, OOB Acc: 90.91%, # of Haplo: 27
    11, SNP: 158, Loss: 16.9514, OOB Acc: 90.91%, # of Haplo: 29
    12, SNP: 247, Loss: 15.5658, OOB Acc: 90.91%, # of Haplo: 35
    13, SNP: 193, Loss: 15.4994, OOB Acc: 90.91%, # of Haplo: 44
    14, SNP: 73, Loss: 12.4986, OOB Acc: 90.91%, # of Haplo: 45
    15, SNP: 199, Loss: 11.7779, OOB Acc: 90.91%, # of Haplo: 45
    16, SNP: 151, Loss: 11.0487, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:03:44, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 45
     1, SNP: 171, Loss: 169.639, OOB Acc: 56.67%, # of Haplo: 14
     2, SNP: 127, Loss: 146.326, OOB Acc: 63.33%, # of Haplo: 16
     3, SNP: 109, Loss: 104.178, OOB Acc: 66.67%, # of Haplo: 18
     4, SNP: 107, Loss: 90.6768, OOB Acc: 73.33%, # of Haplo: 20
     5, SNP: 163, Loss: 66.9122, OOB Acc: 76.67%, # of Haplo: 25
     6, SNP: 185, Loss: 60.1057, OOB Acc: 76.67%, # of Haplo: 33
     7, SNP: 99, Loss: 55.2312, OOB Acc: 76.67%, # of Haplo: 39
     8, SNP: 199, Loss: 47.686, OOB Acc: 76.67%, # of Haplo: 39
     9, SNP: 95, Loss: 46.9138, OOB Acc: 80.00%, # of Haplo: 47
    10, SNP: 70, Loss: 45.9845, OOB Acc: 80.00%, # of Haplo: 49
    11, SNP: 129, Loss: 45.86, OOB Acc: 83.33%, # of Haplo: 49
[2] 2019-04-13 03:03:44, OOB Acc: 83.33%, # of SNPs: 11, # of Haplo: 49
     1, SNP: 122, Loss: 162.121, OOB Acc: 50.00%, # of Haplo: 12
     2, SNP: 137, Loss: 155.367, OOB Acc: 58.33%, # of Haplo: 12
     3, SNP: 81, Loss: 120.826, OOB Acc: 58.33%, # of Haplo: 16
     4, SNP: 146, Loss: 83.2015, OOB Acc: 62.50%, # of Haplo: 21
     5, SNP: 148, Loss: 68.8368, OOB Acc: 62.50%, # of Haplo: 21
     6, SNP: 51, Loss: 40.3043, OOB Acc: 62.50%, # of Haplo: 21
     7, SNP: 231, Loss: 29.1968, OOB Acc: 62.50%, # of Haplo: 23
     8, SNP: 87, Loss: 23.3916, OOB Acc: 62.50%, # of Haplo: 31
     9, SNP: 160, Loss: 20.422, OOB Acc: 62.50%, # of Haplo: 31
    10, SNP: 119, Loss: 20.2055, OOB Acc: 62.50%, # of Haplo: 32
    11, SNP: 9, Loss: 19.9994, OOB Acc: 62.50%, # of Haplo: 37
    12, SNP: 199, Loss: 19.2624, OOB Acc: 62.50%, # of Haplo: 38
    13, SNP: 136, Loss: 19.0415, OOB Acc: 62.50%, # of Haplo: 40
    14, SNP: 151, Loss: 18.6147, OOB Acc: 62.50%, # of Haplo: 40
    15, SNP: 3, Loss: 18.5586, OOB Acc: 62.50%, # of Haplo: 45
    16, SNP: 73, Loss: 14.2585, OOB Acc: 62.50%, # of Haplo: 46
[3] 2019-04-13 03:03:45, OOB Acc: 62.50%, # of SNPs: 16, # of Haplo: 46
     1, SNP: 122, Loss: 185.298, OOB Acc: 75.00%, # of Haplo: 13
     2, SNP: 118, Loss: 178.626, OOB Acc: 83.33%, # of Haplo: 13
     3, SNP: 131, Loss: 149.969, OOB Acc: 87.50%, # of Haplo: 14
     4, SNP: 144, Loss: 97.8546, OOB Acc: 87.50%, # of Haplo: 14
     5, SNP: 123, Loss: 62.1051, OOB Acc: 91.67%, # of Haplo: 14
     6, SNP: 105, Loss: 48.1095, OOB Acc: 95.83%, # of Haplo: 14
     7, SNP: 197, Loss: 30.2151, OOB Acc: 95.83%, # of Haplo: 16
     8, SNP: 40, Loss: 28.3663, OOB Acc: 95.83%, # of Haplo: 20
     9, SNP: 90, Loss: 24.3188, OOB Acc: 95.83%, # of Haplo: 22
    10, SNP: 263, Loss: 19.7509, OOB Acc: 95.83%, # of Haplo: 31
    11, SNP: 67, Loss: 15.2521, OOB Acc: 95.83%, # of Haplo: 31
    12, SNP: 199, Loss: 14.3355, OOB Acc: 95.83%, # of Haplo: 33
    13, SNP: 73, Loss: 9.42737, OOB Acc: 95.83%, # of Haplo: 33
[4] 2019-04-13 03:03:45, OOB Acc: 95.83%, # of SNPs: 13, # of Haplo: 33
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
8.110187e-05 8.734649e-05 1.435481e-04 3.012736e-03 6.600797e-03 4.079281e-02 
        Max.         Mean           SD 
4.833812e-01 4.385633e-02 1.130754e-01 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 83.1%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 46
    avg. # of SNPs in an individual classifier: 14.00
        (sd: 2.45, min: 11, max: 16, median: 14.50)
    avg. # of haplotypes in an individual classifier: 43.25
        (sd: 7.04, min: 33, max: 49, median: 45.50)
    avg. out-of-bag accuracy: 83.14%
        (sd: 14.69%, min: 62.50%, max: 95.83%, median: 87.12%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
8.110187e-05 8.734649e-05 1.435481e-04 3.012736e-03 6.600797e-03 4.079281e-02 
        Max.         Mean           SD 
4.833812e-01 4.385633e-02 1.130754e-01 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:45)	0%
Predicting (2019-04-13 03:03:45)	100%
Allele	Num.	Freq.	Num.	Freq.	CR	ACC	SEN	SPE	PPV	NPV	Miscall
	Train	Train	Valid.	Valid.	(%)	(%)	(%)	(%)	(%)	(%)	(%)
----
Overall accuracy: 94.2%, Call rate: 100.0%
01:01 13 0.1912 12 0.2308 100.0 100.0 100.0 100.0 100.0 100.0 --
02:01 23 0.3382 20 0.3846 100.0 100.0 100.0 100.0 100.0 100.0 --
02:06 1 0.0147 0 0 -- -- -- -- -- -- --
03:01 4 0.0588 5 0.0962 100.0 98.1 80.0 100.0 100.0 97.9 24:02 (100)
11:01 3 0.0441 2 0.0385 100.0 100.0 100.0 100.0 100.0 100.0 --
23:01 2 0.0294 1 0.0192 100.0 98.1 100.0 98.0 50.0 100.0 --
24:02 6 0.0882 5 0.0962 100.0 96.2 80.0 97.9 80.0 97.9 23:01 (100)
24:03 1 0.0147 0 0 -- -- -- -- -- -- --
25:01 3 0.0441 2 0.0385 100.0 98.1 50.0 100.0 100.0 98.0 26:01 (100)
26:01 2 0.0294 1 0.0192 100.0 98.1 100.0 98.0 50.0 100.0 --
29:02 3 0.0441 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
31:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
32:01 3 0.0441 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
68:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
\title{Imputation Evaluation}

\documentclass[12pt]{article}

\usepackage{fullpage}
\usepackage{longtable}

\begin{document}

\maketitle

\setlength{\LTcapwidth}{6.5in}

% -------- BEGIN TABLE --------
\begin{longtable}{rrrrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
 & Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{12}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
 & Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{12}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{12}{l}{\it Overall accuracy: 94.2\%, Call rate: 100.0\%} \\
01:01 & 13 & 0.1912 & 12 & 0.2308 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
02:01 & 23 & 0.3382 & 20 & 0.3846 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
02:06 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
03:01 & 4 & 0.0588 & 5 & 0.0962 & 100.0 & 98.1 & 80.0 & 100.0 & 100.0 & 97.9 & 24:02 (100) \\
11:01 & 3 & 0.0441 & 2 & 0.0385 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
23:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 100.0 & 98.0 & 50.0 & 100.0 & -- \\
24:02 & 6 & 0.0882 & 5 & 0.0962 & 100.0 & 96.2 & 80.0 & 97.9 & 80.0 & 97.9 & 23:01 (100) \\
24:03 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
25:01 & 3 & 0.0441 & 2 & 0.0385 & 100.0 & 98.1 & 50.0 & 100.0 & 100.0 & 98.0 & 26:01 (100) \\
26:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 100.0 & 98.0 & 50.0 & 100.0 & -- \\
29:02 & 3 & 0.0441 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
31:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
32:01 & 3 & 0.0441 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
\end{longtable}
% -------- END TABLE --------

\end{document}
<!DOCTYPE html>
<html>
<head>
  <title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1"  CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Train</th> <th>Freq. Train</th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="12">
<i> Overall accuracy: 94.2%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>13</td> <td>0.1912</td> <td>12</td> <td>0.2308</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>23</td> <td>0.3382</td> <td>20</td> <td>0.3846</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>03:01</td> <td>4</td> <td>0.0588</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>98.1</td> <td>80.0</td> <td>100.0</td> <td>100.0</td> <td>97.9</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>11:01</td> <td>3</td> <td>0.0441</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>98.0</td> <td>50.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>24:02</td> <td>6</td> <td>0.0882</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>96.2</td> <td>80.0</td> <td>97.9</td> <td>80.0</td> <td>97.9</td> <td>23:01 (100)</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>25:01</td> <td>3</td> <td>0.0441</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>98.1</td> <td>50.0</td> <td>100.0</td> <td>100.0</td> <td>98.0</td> <td>26:01 (100)</td>
</tr>
<tr>
<td>26:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>98.0</td> <td>50.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>29:02</td> <td>3</td> <td>0.0441</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>31:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>32:01</td> <td>3</td> <td>0.0441</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
</table>

</body>
</html>
**Overall accuracy: 94.2%, Call rate: 100.0%**

| Allele  | # Train | Freq. Train | # Valid. | Freq. Valid. | CR (%) | ACC (%) | SEN (%) | SPE (%) | PPV (%) | NPV (%) | Miscall (%) |
|:--|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|:--|
| 01:01 | 13 | 0.1912 | 12 | 0.2308 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 02:01 | 23 | 0.3382 | 20 | 0.3846 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 02:06 |  1 | 0.0147 |  0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 03:01 |  4 | 0.0588 |  5 | 0.0962 | 100.0 | 98.1 | 80.0 | 100.0 | 100.0 | 97.9 | 24:02 (100) |
| 11:01 |  3 | 0.0441 |  2 | 0.0385 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 23:01 |  2 | 0.0294 |  1 | 0.0192 | 100.0 | 98.1 | 100.0 | 98.0 | 50.0 | 100.0 | -- |
| 24:02 |  6 | 0.0882 |  5 | 0.0962 | 100.0 | 96.2 | 80.0 | 97.9 | 80.0 | 97.9 | 23:01 (100) |
| 24:03 |  1 | 0.0147 |  0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 25:01 |  3 | 0.0441 |  2 | 0.0385 | 100.0 | 98.1 | 50.0 | 100.0 | 100.0 | 98.0 | 26:01 (100) |
| 26:01 |  2 | 0.0294 |  1 | 0.0192 | 100.0 | 98.1 | 100.0 | 98.0 | 50.0 | 100.0 | -- |
| 29:02 |  3 | 0.0441 |  1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 31:01 |  2 | 0.0294 |  1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 32:01 |  3 | 0.0441 |  1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 68:01 |  2 | 0.0294 |  1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:45
     1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
     2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
     3, SNP: 204, Loss: 130.453, OOB Acc: 72.73%, # of Haplo: 16
     4, SNP: 87, Loss: 81.1116, OOB Acc: 77.27%, # of Haplo: 20
     5, SNP: 115, Loss: 60.8965, OOB Acc: 86.36%, # of Haplo: 21
     6, SNP: 191, Loss: 35.3274, OOB Acc: 90.91%, # of Haplo: 22
     7, SNP: 220, Loss: 23.2514, OOB Acc: 90.91%, # of Haplo: 24
     8, SNP: 47, Loss: 19.9968, OOB Acc: 90.91%, # of Haplo: 25
     9, SNP: 20, Loss: 18.0597, OOB Acc: 90.91%, # of Haplo: 27
    10, SNP: 171, Loss: 17.3174, OOB Acc: 90.91%, # of Haplo: 27
    11, SNP: 158, Loss: 16.9514, OOB Acc: 90.91%, # of Haplo: 29
    12, SNP: 247, Loss: 15.5658, OOB Acc: 90.91%, # of Haplo: 35
    13, SNP: 193, Loss: 15.4994, OOB Acc: 90.91%, # of Haplo: 44
    14, SNP: 73, Loss: 12.4986, OOB Acc: 90.91%, # of Haplo: 45
    15, SNP: 199, Loss: 11.7779, OOB Acc: 90.91%, # of Haplo: 45
    16, SNP: 151, Loss: 11.0487, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:03:45, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 45
     1, SNP: 171, Loss: 169.639, OOB Acc: 56.67%, # of Haplo: 14
     2, SNP: 127, Loss: 146.326, OOB Acc: 63.33%, # of Haplo: 16
     3, SNP: 109, Loss: 104.178, OOB Acc: 66.67%, # of Haplo: 18
     4, SNP: 107, Loss: 90.6768, OOB Acc: 73.33%, # of Haplo: 20
     5, SNP: 163, Loss: 66.9122, OOB Acc: 76.67%, # of Haplo: 25
     6, SNP: 185, Loss: 60.1057, OOB Acc: 76.67%, # of Haplo: 33
     7, SNP: 99, Loss: 55.2312, OOB Acc: 76.67%, # of Haplo: 39
     8, SNP: 199, Loss: 47.686, OOB Acc: 76.67%, # of Haplo: 39
     9, SNP: 95, Loss: 46.9138, OOB Acc: 80.00%, # of Haplo: 47
    10, SNP: 70, Loss: 45.9845, OOB Acc: 80.00%, # of Haplo: 49
    11, SNP: 129, Loss: 45.86, OOB Acc: 83.33%, # of Haplo: 49
[2] 2019-04-13 03:03:45, OOB Acc: 83.33%, # of SNPs: 11, # of Haplo: 49
     1, SNP: 122, Loss: 162.121, OOB Acc: 50.00%, # of Haplo: 12
     2, SNP: 137, Loss: 155.367, OOB Acc: 58.33%, # of Haplo: 12
     3, SNP: 81, Loss: 120.826, OOB Acc: 58.33%, # of Haplo: 16
     4, SNP: 146, Loss: 83.2015, OOB Acc: 62.50%, # of Haplo: 21
     5, SNP: 148, Loss: 68.8368, OOB Acc: 62.50%, # of Haplo: 21
     6, SNP: 51, Loss: 40.3043, OOB Acc: 62.50%, # of Haplo: 21
     7, SNP: 231, Loss: 29.1968, OOB Acc: 62.50%, # of Haplo: 23
     8, SNP: 87, Loss: 23.3916, OOB Acc: 62.50%, # of Haplo: 31
     9, SNP: 160, Loss: 20.422, OOB Acc: 62.50%, # of Haplo: 31
    10, SNP: 119, Loss: 20.2055, OOB Acc: 62.50%, # of Haplo: 32
    11, SNP: 9, Loss: 19.9994, OOB Acc: 62.50%, # of Haplo: 37
    12, SNP: 199, Loss: 19.2624, OOB Acc: 62.50%, # of Haplo: 38
    13, SNP: 136, Loss: 19.0415, OOB Acc: 62.50%, # of Haplo: 40
    14, SNP: 151, Loss: 18.6147, OOB Acc: 62.50%, # of Haplo: 40
    15, SNP: 3, Loss: 18.5586, OOB Acc: 62.50%, # of Haplo: 45
    16, SNP: 73, Loss: 14.2585, OOB Acc: 62.50%, # of Haplo: 46
[3] 2019-04-13 03:03:45, OOB Acc: 62.50%, # of SNPs: 16, # of Haplo: 46
     1, SNP: 122, Loss: 185.298, OOB Acc: 75.00%, # of Haplo: 13
     2, SNP: 118, Loss: 178.626, OOB Acc: 83.33%, # of Haplo: 13
     3, SNP: 131, Loss: 149.969, OOB Acc: 87.50%, # of Haplo: 14
     4, SNP: 144, Loss: 97.8546, OOB Acc: 87.50%, # of Haplo: 14
     5, SNP: 123, Loss: 62.1051, OOB Acc: 91.67%, # of Haplo: 14
     6, SNP: 105, Loss: 48.1095, OOB Acc: 95.83%, # of Haplo: 14
     7, SNP: 197, Loss: 30.2151, OOB Acc: 95.83%, # of Haplo: 16
     8, SNP: 40, Loss: 28.3663, OOB Acc: 95.83%, # of Haplo: 20
     9, SNP: 90, Loss: 24.3188, OOB Acc: 95.83%, # of Haplo: 22
    10, SNP: 263, Loss: 19.7509, OOB Acc: 95.83%, # of Haplo: 31
    11, SNP: 67, Loss: 15.2521, OOB Acc: 95.83%, # of Haplo: 31
    12, SNP: 199, Loss: 14.3355, OOB Acc: 95.83%, # of Haplo: 33
    13, SNP: 73, Loss: 9.42737, OOB Acc: 95.83%, # of Haplo: 33
[4] 2019-04-13 03:03:45, OOB Acc: 95.83%, # of SNPs: 13, # of Haplo: 33
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
8.110187e-05 8.734649e-05 1.435481e-04 3.012736e-03 6.600797e-03 4.079281e-02 
        Max.         Mean           SD 
4.833812e-01 4.385633e-02 1.130754e-01 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 83.1%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 46
    avg. # of SNPs in an individual classifier: 14.00
        (sd: 2.45, min: 11, max: 16, median: 14.50)
    avg. # of haplotypes in an individual classifier: 43.25
        (sd: 7.04, min: 33, max: 49, median: 45.50)
    avg. out-of-bag accuracy: 83.14%
        (sd: 14.69%, min: 62.50%, max: 95.83%, median: 87.12%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
8.110187e-05 8.734649e-05 1.435481e-04 3.012736e-03 6.600797e-03 4.079281e-02 
        Max.         Mean           SD 
4.833812e-01 4.385633e-02 1.130754e-01 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:45)	0%
Predicting (2019-04-13 03:03:45)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 8
# of SNPs: 51, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:46
     1, SNP: 13, Loss: 391.274, OOB Acc: 41.67%, # of Haplo: 17
     2, SNP: 2, Loss: 321.685, OOB Acc: 52.08%, # of Haplo: 18
     3, SNP: 36, Loss: 232.846, OOB Acc: 58.33%, # of Haplo: 19
     4, SNP: 28, Loss: 178.077, OOB Acc: 62.50%, # of Haplo: 20
     5, SNP: 35, Loss: 107.151, OOB Acc: 68.75%, # of Haplo: 20
     6, SNP: 3, Loss: 72.2736, OOB Acc: 72.92%, # of Haplo: 23
     7, SNP: 19, Loss: 50.8439, OOB Acc: 77.08%, # of Haplo: 25
     8, SNP: 4, Loss: 47.2744, OOB Acc: 83.33%, # of Haplo: 29
     9, SNP: 10, Loss: 40.6216, OOB Acc: 83.33%, # of Haplo: 29
    10, SNP: 37, Loss: 38.9199, OOB Acc: 83.33%, # of Haplo: 30
    11, SNP: 26, Loss: 32.7795, OOB Acc: 83.33%, # of Haplo: 38
    12, SNP: 5, Loss: 31.6837, OOB Acc: 83.33%, # of Haplo: 44
    13, SNP: 29, Loss: 31.6837, OOB Acc: 85.42%, # of Haplo: 44
[1] 2019-04-13 03:03:46, OOB Acc: 85.42%, # of SNPs: 13, # of Haplo: 44
     1, SNP: 18, Loss: 467.346, OOB Acc: 42.50%, # of Haplo: 17
     2, SNP: 51, Loss: 397.716, OOB Acc: 50.00%, # of Haplo: 18
     3, SNP: 4, Loss: 286.978, OOB Acc: 55.00%, # of Haplo: 18
     4, SNP: 50, Loss: 224.305, OOB Acc: 62.50%, # of Haplo: 19
     5, SNP: 34, Loss: 147.479, OOB Acc: 70.00%, # of Haplo: 19
     6, SNP: 29, Loss: 139.44, OOB Acc: 75.00%, # of Haplo: 19
     7, SNP: 20, Loss: 74.4196, OOB Acc: 80.00%, # of Haplo: 20
     8, SNP: 49, Loss: 72.6051, OOB Acc: 82.50%, # of Haplo: 20
     9, SNP: 1, Loss: 41.7053, OOB Acc: 85.00%, # of Haplo: 20
    10, SNP: 32, Loss: 38.8112, OOB Acc: 85.00%, # of Haplo: 20
    11, SNP: 7, Loss: 43.6126, OOB Acc: 87.50%, # of Haplo: 21
    12, SNP: 36, Loss: 44.1728, OOB Acc: 92.50%, # of Haplo: 26
    13, SNP: 37, Loss: 43.1334, OOB Acc: 92.50%, # of Haplo: 27
    14, SNP: 11, Loss: 41.7533, OOB Acc: 92.50%, # of Haplo: 25
[2] 2019-04-13 03:03:46, OOB Acc: 92.50%, # of SNPs: 14, # of Haplo: 25
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.170686e-05 2.247847e-05 2.942300e-05 4.618178e-03 1.769730e-02 5.457802e-02 
        Max.         Mean           SD 
1.595681e-01 3.334436e-02 3.729916e-02 
Accuracy with training data: 93.3%
Out-of-bag accuracy: 89.0%
Gene: C
Training dataset: 60 samples X 51 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 2
    total # of SNPs used: 23
    avg. # of SNPs in an individual classifier: 13.50
        (sd: 0.71, min: 13, max: 14, median: 13.50)
    avg. # of haplotypes in an individual classifier: 34.50
        (sd: 13.44, min: 25, max: 44, median: 34.50)
    avg. out-of-bag accuracy: 88.96%
        (sd: 5.01%, min: 85.42%, max: 92.50%, median: 88.96%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.170686e-05 2.247847e-05 2.942300e-05 4.618178e-03 1.769730e-02 5.457802e-02 
        Max.         Mean           SD 
1.595681e-01 3.334436e-02 3.729916e-02 
Genome assembly: hg19
Gene: C
Training dataset: 60 samples X 51 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 1
    total # of SNPs used: 13
    avg. # of SNPs in an individual classifier: 13.00
        (sd: NA, min: 13, max: 13, median: 13.00)
    avg. # of haplotypes in an individual classifier: 44.00
        (sd: NA, min: 44, max: 44, median: 44.00)
    avg. out-of-bag accuracy: 85.42%
        (sd: NA%, min: 85.42%, max: 85.42%, median: 85.42%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.170686e-05 2.247847e-05 2.942300e-05 4.618178e-03 1.769730e-02 5.457802e-02 
        Max.         Mean           SD 
1.595681e-01 3.334436e-02 3.729916e-02 
Genome assembly: hg19
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:46
     1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17
     2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17
     3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20
     4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20
     5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22
     6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24
     7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24
     8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22
     9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24
    10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24
    11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28
    12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29
    13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37
    14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38
    15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39
    16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40
    17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41
    18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43
    19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43
[1] 2019-04-13 03:03:46, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43
     1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19
     2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21
     3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21
     4, SNP: 59, Loss: 209.932, OOB Acc: 76.47%, # of Haplo: 21
     5, SNP: 74, Loss: 153.579, OOB Acc: 82.35%, # of Haplo: 22
     6, SNP: 69, Loss: 108.679, OOB Acc: 88.24%, # of Haplo: 22
     7, SNP: 72, Loss: 81.3125, OOB Acc: 91.18%, # of Haplo: 22
     8, SNP: 24, Loss: 67.8072, OOB Acc: 91.18%, # of Haplo: 22
     9, SNP: 70, Loss: 56.7061, OOB Acc: 91.18%, # of Haplo: 22
    10, SNP: 82, Loss: 55.1169, OOB Acc: 94.12%, # of Haplo: 26
    11, SNP: 3, Loss: 53.5756, OOB Acc: 94.12%, # of Haplo: 27
    12, SNP: 2, Loss: 45.9376, OOB Acc: 94.12%, # of Haplo: 29
    13, SNP: 27, Loss: 26.806, OOB Acc: 94.12%, # of Haplo: 29
    14, SNP: 5, Loss: 25.3407, OOB Acc: 94.12%, # of Haplo: 30
    15, SNP: 32, Loss: 22.6034, OOB Acc: 94.12%, # of Haplo: 31
    16, SNP: 78, Loss: 21.7748, OOB Acc: 94.12%, # of Haplo: 37
[2] 2019-04-13 03:03:46, OOB Acc: 94.12%, # of SNPs: 16, # of Haplo: 37
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
1.944655e-08 6.164670e-06 6.147168e-05 1.555809e-03 6.481833e-03 3.204650e-02 
        Max.         Mean           SD 
7.898042e-02 1.807739e-02 2.114254e-02 
Accuracy with training data: 96.7%
Out-of-bag accuracy: 91.9%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:46
     1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
     2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
     3, SNP: 204, Loss: 130.453, OOB Acc: 72.73%, # of Haplo: 16
     4, SNP: 87, Loss: 81.1116, OOB Acc: 77.27%, # of Haplo: 20
     5, SNP: 115, Loss: 60.8965, OOB Acc: 86.36%, # of Haplo: 21
     6, SNP: 191, Loss: 35.3274, OOB Acc: 90.91%, # of Haplo: 22
     7, SNP: 220, Loss: 23.2514, OOB Acc: 90.91%, # of Haplo: 24
     8, SNP: 47, Loss: 19.9968, OOB Acc: 90.91%, # of Haplo: 25
     9, SNP: 20, Loss: 18.0597, OOB Acc: 90.91%, # of Haplo: 27
    10, SNP: 171, Loss: 17.3174, OOB Acc: 90.91%, # of Haplo: 27
    11, SNP: 158, Loss: 16.9514, OOB Acc: 90.91%, # of Haplo: 29
    12, SNP: 247, Loss: 15.5658, OOB Acc: 90.91%, # of Haplo: 35
    13, SNP: 193, Loss: 15.4994, OOB Acc: 90.91%, # of Haplo: 44
    14, SNP: 73, Loss: 12.4986, OOB Acc: 90.91%, # of Haplo: 45
    15, SNP: 199, Loss: 11.7779, OOB Acc: 90.91%, # of Haplo: 45
    16, SNP: 151, Loss: 11.0487, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:03:46, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 45
     1, SNP: 171, Loss: 169.639, OOB Acc: 56.67%, # of Haplo: 14
     2, SNP: 127, Loss: 146.326, OOB Acc: 63.33%, # of Haplo: 16
     3, SNP: 109, Loss: 104.178, OOB Acc: 66.67%, # of Haplo: 18
     4, SNP: 107, Loss: 90.6768, OOB Acc: 73.33%, # of Haplo: 20
     5, SNP: 163, Loss: 66.9122, OOB Acc: 76.67%, # of Haplo: 25
     6, SNP: 185, Loss: 60.1057, OOB Acc: 76.67%, # of Haplo: 33
     7, SNP: 99, Loss: 55.2312, OOB Acc: 76.67%, # of Haplo: 39
     8, SNP: 199, Loss: 47.686, OOB Acc: 76.67%, # of Haplo: 39
     9, SNP: 95, Loss: 46.9138, OOB Acc: 80.00%, # of Haplo: 47
    10, SNP: 70, Loss: 45.9845, OOB Acc: 80.00%, # of Haplo: 49
    11, SNP: 129, Loss: 45.86, OOB Acc: 83.33%, # of Haplo: 49
[2] 2019-04-13 03:03:46, OOB Acc: 83.33%, # of SNPs: 11, # of Haplo: 49
     1, SNP: 122, Loss: 162.121, OOB Acc: 50.00%, # of Haplo: 12
     2, SNP: 137, Loss: 155.367, OOB Acc: 58.33%, # of Haplo: 12
     3, SNP: 81, Loss: 120.826, OOB Acc: 58.33%, # of Haplo: 16
     4, SNP: 146, Loss: 83.2015, OOB Acc: 62.50%, # of Haplo: 21
     5, SNP: 148, Loss: 68.8368, OOB Acc: 62.50%, # of Haplo: 21
     6, SNP: 51, Loss: 40.3043, OOB Acc: 62.50%, # of Haplo: 21
     7, SNP: 231, Loss: 29.1968, OOB Acc: 62.50%, # of Haplo: 23
     8, SNP: 87, Loss: 23.3916, OOB Acc: 62.50%, # of Haplo: 31
     9, SNP: 160, Loss: 20.422, OOB Acc: 62.50%, # of Haplo: 31
    10, SNP: 119, Loss: 20.2055, OOB Acc: 62.50%, # of Haplo: 32
    11, SNP: 9, Loss: 19.9994, OOB Acc: 62.50%, # of Haplo: 37
    12, SNP: 199, Loss: 19.2624, OOB Acc: 62.50%, # of Haplo: 38
    13, SNP: 136, Loss: 19.0415, OOB Acc: 62.50%, # of Haplo: 40
    14, SNP: 151, Loss: 18.6147, OOB Acc: 62.50%, # of Haplo: 40
    15, SNP: 3, Loss: 18.5586, OOB Acc: 62.50%, # of Haplo: 45
    16, SNP: 73, Loss: 14.2585, OOB Acc: 62.50%, # of Haplo: 46
[3] 2019-04-13 03:03:46, OOB Acc: 62.50%, # of SNPs: 16, # of Haplo: 46
     1, SNP: 122, Loss: 185.298, OOB Acc: 75.00%, # of Haplo: 13
     2, SNP: 118, Loss: 178.626, OOB Acc: 83.33%, # of Haplo: 13
     3, SNP: 131, Loss: 149.969, OOB Acc: 87.50%, # of Haplo: 14
     4, SNP: 144, Loss: 97.8546, OOB Acc: 87.50%, # of Haplo: 14
     5, SNP: 123, Loss: 62.1051, OOB Acc: 91.67%, # of Haplo: 14
     6, SNP: 105, Loss: 48.1095, OOB Acc: 95.83%, # of Haplo: 14
     7, SNP: 197, Loss: 30.2151, OOB Acc: 95.83%, # of Haplo: 16
     8, SNP: 40, Loss: 28.3663, OOB Acc: 95.83%, # of Haplo: 20
     9, SNP: 90, Loss: 24.3188, OOB Acc: 95.83%, # of Haplo: 22
    10, SNP: 263, Loss: 19.7509, OOB Acc: 95.83%, # of Haplo: 31
    11, SNP: 67, Loss: 15.2521, OOB Acc: 95.83%, # of Haplo: 31
    12, SNP: 199, Loss: 14.3355, OOB Acc: 95.83%, # of Haplo: 33
    13, SNP: 73, Loss: 9.42737, OOB Acc: 95.83%, # of Haplo: 33
[4] 2019-04-13 03:03:46, OOB Acc: 95.83%, # of SNPs: 13, # of Haplo: 33
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
8.110187e-05 8.734649e-05 1.435481e-04 3.012736e-03 6.600797e-03 4.079281e-02 
        Max.         Mean           SD 
4.833812e-01 4.385633e-02 1.130754e-01 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 83.1%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 46
    avg. # of SNPs in an individual classifier: 14.00
        (sd: 2.45, min: 11, max: 16, median: 14.50)
    avg. # of haplotypes in an individual classifier: 43.25
        (sd: 7.04, min: 33, max: 49, median: 45.50)
    avg. out-of-bag accuracy: 83.14%
        (sd: 14.69%, min: 62.50%, max: 95.83%, median: 87.12%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
8.110187e-05 8.734649e-05 1.435481e-04 3.012736e-03 6.600797e-03 4.079281e-02 
        Max.         Mean           SD 
4.833812e-01 4.385633e-02 1.130754e-01 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:46)	0%
Predicting (2019-04-13 03:03:46)	100%
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:46
     1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17
     2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17
     3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20
     4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20
     5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22
     6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24
     7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24
     8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22
     9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24
    10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24
    11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28
    12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29
    13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37
    14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38
    15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39
    16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40
    17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41
    18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43
    19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43
[1] 2019-04-13 03:03:46, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43
     1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19
     2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21
     3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21
     4, SNP: 59, Loss: 209.932, OOB Acc: 76.47%, # of Haplo: 21
     5, SNP: 74, Loss: 153.579, OOB Acc: 82.35%, # of Haplo: 22
     6, SNP: 69, Loss: 108.679, OOB Acc: 88.24%, # of Haplo: 22
     7, SNP: 72, Loss: 81.3125, OOB Acc: 91.18%, # of Haplo: 22
     8, SNP: 24, Loss: 67.8072, OOB Acc: 91.18%, # of Haplo: 22
     9, SNP: 70, Loss: 56.7061, OOB Acc: 91.18%, # of Haplo: 22
    10, SNP: 82, Loss: 55.1169, OOB Acc: 94.12%, # of Haplo: 26
    11, SNP: 3, Loss: 53.5756, OOB Acc: 94.12%, # of Haplo: 27
    12, SNP: 2, Loss: 45.9376, OOB Acc: 94.12%, # of Haplo: 29
    13, SNP: 27, Loss: 26.806, OOB Acc: 94.12%, # of Haplo: 29
    14, SNP: 5, Loss: 25.3407, OOB Acc: 94.12%, # of Haplo: 30
    15, SNP: 32, Loss: 22.6034, OOB Acc: 94.12%, # of Haplo: 31
    16, SNP: 78, Loss: 21.7748, OOB Acc: 94.12%, # of Haplo: 37
[2] 2019-04-13 03:03:46, OOB Acc: 94.12%, # of SNPs: 16, # of Haplo: 37
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
1.944655e-08 6.164670e-06 6.147168e-05 1.555809e-03 6.481833e-03 3.204650e-02 
        Max.         Mean           SD 
7.898042e-02 1.807739e-02 2.114254e-02 
Accuracy with training data: 96.7%
Out-of-bag accuracy: 91.9%
Gene: C
Training dataset: 60 samples X 83 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 2
    total # of SNPs used: 27
    avg. # of SNPs in an individual classifier: 17.50
        (sd: 2.12, min: 16, max: 19, median: 17.50)
    avg. # of haplotypes in an individual classifier: 40.00
        (sd: 4.24, min: 37, max: 43, median: 40.00)
    avg. out-of-bag accuracy: 91.85%
        (sd: 3.21%, min: 89.58%, max: 94.12%, median: 91.85%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
1.944655e-08 6.164670e-06 6.147168e-05 1.555809e-03 6.481833e-03 3.204650e-02 
        Max.         Mean           SD 
7.898042e-02 1.807739e-02 2.114254e-02 
Genome assembly: hg19
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
> 
> proc.time()
   user  system elapsed 
  27.25    0.45   29.48 

HIBAG.Rcheck/tests_x64/runTests.Rout


R version 3.5.3 (2019-03-11) -- "Great Truth"
Copyright (C) 2019 The R Foundation for Statistical Computing
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> #############################################################
> #
> # DESCRIPTION: Unit tests in the HIBAG package
> #
> 
> # load the HIBAG package
> library(HIBAG)
HIBAG (HLA Genotype Imputation with Attribute Bagging)
Kernel Version: v1.4
Supported by Streaming SIMD Extensions (SSE2)
> 
> 
> #############################################################
> 
> # a list of HLA genes
> hla.list <- c("A", "B", "C", "DQA1", "DQB1", "DRB1")
> 
> # pre-defined lower bound of prediction accuracy
> hla.acc <- c(0.9, 0.8, 0.8, 0.85, 0.85, 0.75)
> 
> 
> for (hla.idx in seq_len(length(hla.list)))
+ {
+ 	hla.id <- hla.list[hla.idx]
+ 
+ 	# make a "hlaAlleleClass" object
+ 	hla <- hlaAllele(HLA_Type_Table$sample.id,
+ 		H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")],
+ 		H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")],
+ 		locus=hla.id, assembly="hg19")
+ 
+ 	# divide HLA types randomly
+ 	set.seed(100)
+ 	hlatab <- hlaSplitAllele(hla, train.prop=0.5)
+ 
+ 	# SNP predictors within the flanking region on each side
+ 	region <- 500	# kb
+ 	snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id,
+ 		HapMap_CEU_Geno$snp.position,
+ 		hla.id, region*1000, assembly="hg19")
+ 
+ 	# training and validation genotypes
+ 	train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
+ 		snp.sel=match(snpid, HapMap_CEU_Geno$snp.id),
+ 		samp.sel=match(hlatab$training$value$sample.id,
+ 		HapMap_CEU_Geno$sample.id))
+ 	test.geno <- hlaGenoSubset(HapMap_CEU_Geno,
+ 		samp.sel=match(hlatab$validation$value$sample.id,
+ 		HapMap_CEU_Geno$sample.id))
+ 
+ 
+ 	# train a HIBAG model
+ 	set.seed(100)
+ 	model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=10)
+ 	summary(model)
+ 
+ 	# validation
+ 	pred <- predict(model, test.geno)
+ 	summary(pred)
+ 
+ 	# compare
+ 	comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
+ 		call.threshold=0)
+ 	print(comp$overall)
+ 
+ 	# check
+ 	if (comp$overall$acc.haplo < hla.acc[hla.idx])
+ 	{
+ 		stop("HLA - ", hla.id, ", 'acc.haplo' should be >= ",
+ 			hla.acc[hla.idx], ".")
+ 	}
+ 
+ 	cat("\n\n")
+ }
Exclude 9 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:47
[1] 2019-04-13 03:03:47, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
[2] 2019-04-13 03:03:47, OOB Acc: 92.31%, # of SNPs: 20, # of Haplo: 48
[3] 2019-04-13 03:03:47, OOB Acc: 91.18%, # of SNPs: 18, # of Haplo: 45
[4] 2019-04-13 03:03:47, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 39
[5] 2019-04-13 03:03:47, OOB Acc: 83.33%, # of SNPs: 13, # of Haplo: 31
[6] 2019-04-13 03:03:47, OOB Acc: 94.44%, # of SNPs: 15, # of Haplo: 27
[7] 2019-04-13 03:03:48, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 56
[8] 2019-04-13 03:03:48, OOB Acc: 81.82%, # of SNPs: 17, # of Haplo: 36
[9] 2019-04-13 03:03:48, OOB Acc: 90.91%, # of SNPs: 18, # of Haplo: 75
[10] 2019-04-13 03:03:48, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 55
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004866021 0.0004930254 0.0005508348 0.0035299669 0.0069578428 0.0182374201 
        Max.         Mean           SD 
0.3535622480 0.0343318466 0.0826524583 
Accuracy with training data: 98.5%
Out-of-bag accuracy: 89.4%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 10
    total # of SNPs used: 104
    avg. # of SNPs in an individual classifier: 16.20
        (sd: 2.04, min: 13, max: 20, median: 15.50)
    avg. # of haplotypes in an individual classifier: 45.40
        (sd: 14.04, min: 27, max: 75, median: 43.50)
    avg. out-of-bag accuracy: 89.42%
        (sd: 4.00%, min: 81.82%, max: 94.44%, median: 90.91%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004866021 0.0004930254 0.0005508348 0.0035299669 0.0069578428 0.0182374201 
        Max.         Mean           SD 
0.3535622480 0.0343318466 0.0826524583 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:48)	0%
Predicting (2019-04-13 03:03:48)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.8%)   2 (7.7%)  7 (26.9%) 16 (61.5%) 
Matching proportion of SNP haplotype:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
0.000000 0.000448 0.006274 0.022911 0.021609 0.353562 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            26          24            50 0.9230769 0.9615385              0
  n.call call.rate
1     26         1


Exclude 1 monomorphic SNP
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 340, # of samples: 28
# of unique HLA alleles: 22
[-] 2019-04-13 03:03:48
[1] 2019-04-13 03:03:49, OOB Acc: 75.00%, # of SNPs: 12, # of Haplo: 121
[2] 2019-04-13 03:03:49, OOB Acc: 60.00%, # of SNPs: 17, # of Haplo: 64
[3] 2019-04-13 03:03:49, OOB Acc: 63.64%, # of SNPs: 18, # of Haplo: 158
[4] 2019-04-13 03:03:49, OOB Acc: 72.73%, # of SNPs: 12, # of Haplo: 49
[5] 2019-04-13 03:03:49, OOB Acc: 72.73%, # of SNPs: 12, # of Haplo: 33
[6] 2019-04-13 03:03:50, OOB Acc: 77.78%, # of SNPs: 17, # of Haplo: 89
[7] 2019-04-13 03:03:50, OOB Acc: 63.64%, # of SNPs: 23, # of Haplo: 121
[8] 2019-04-13 03:03:50, OOB Acc: 50.00%, # of SNPs: 25, # of Haplo: 95
[9] 2019-04-13 03:03:50, OOB Acc: 71.43%, # of SNPs: 18, # of Haplo: 58
[10] 2019-04-13 03:03:51, OOB Acc: 72.22%, # of SNPs: 17, # of Haplo: 99
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
6.377942e-05 8.530385e-05 2.790238e-04 2.040836e-03 4.488214e-03 1.052265e-02 
        Max.         Mean           SD 
1.458034e-01 1.506772e-02 3.127080e-02 
Accuracy with training data: 98.2%
Out-of-bag accuracy: 67.9%
Gene: B
Training dataset: 28 samples X 340 SNPs
    # of HLA alleles: 22
    # of individual classifiers: 10
    total # of SNPs used: 115
    avg. # of SNPs in an individual classifier: 17.10
        (sd: 4.43, min: 12, max: 25, median: 17.00)
    avg. # of haplotypes in an individual classifier: 88.70
        (sd: 38.39, min: 33, max: 158, median: 92.00)
    avg. out-of-bag accuracy: 67.92%
        (sd: 8.46%, min: 50.00%, max: 77.78%, median: 71.83%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
6.377942e-05 8.530385e-05 2.790238e-04 2.040836e-03 4.488214e-03 1.052265e-02 
        Max.         Mean           SD 
1.458034e-01 1.506772e-02 3.127080e-02 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 340 SNPs, 22 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 15
Predicting (2019-04-13 03:03:51)	0%
Predicting (2019-04-13 03:03:51)	100%
Gene: B
Range: [31321649bp, 31324989bp] on hg19
# of samples: 15
# of unique HLA alleles: 11
# of unique HLA genotypes: 13
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
 3 (20.0%)  4 (26.7%)  4 (26.7%)  4 (26.7%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
2.000e-08 2.950e-04 7.655e-04 1.920e-02 1.067e-02 2.065e-01 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            15          11            25 0.7333333 0.8333333              0
  n.call call.rate
1     15         1


Exclude 2 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 354, # of samples: 36
# of unique HLA alleles: 17
[-] 2019-04-13 03:03:51
[1] 2019-04-13 03:03:51, OOB Acc: 96.15%, # of SNPs: 19, # of Haplo: 35
[2] 2019-04-13 03:03:51, OOB Acc: 90.91%, # of SNPs: 25, # of Haplo: 75
[3] 2019-04-13 03:03:51, OOB Acc: 66.67%, # of SNPs: 14, # of Haplo: 69
[4] 2019-04-13 03:03:51, OOB Acc: 76.92%, # of SNPs: 16, # of Haplo: 38
[5] 2019-04-13 03:03:52, OOB Acc: 79.17%, # of SNPs: 25, # of Haplo: 64
[6] 2019-04-13 03:03:52, OOB Acc: 91.67%, # of SNPs: 36, # of Haplo: 52
[7] 2019-04-13 03:03:52, OOB Acc: 92.31%, # of SNPs: 16, # of Haplo: 58
[8] 2019-04-13 03:03:52, OOB Acc: 89.29%, # of SNPs: 19, # of Haplo: 26
[9] 2019-04-13 03:03:52, OOB Acc: 91.67%, # of SNPs: 21, # of Haplo: 71
[10] 2019-04-13 03:03:53, OOB Acc: 81.25%, # of SNPs: 25, # of Haplo: 77
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0003038237 0.0003220623 0.0004862091 0.0018325695 0.0037925315 0.0087410133 
        Max.         Mean           SD 
0.0940510882 0.0104030527 0.0181801883 
Accuracy with training data: 100.0%
Out-of-bag accuracy: 85.6%
Gene: C
Training dataset: 36 samples X 354 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 10
    total # of SNPs used: 146
    avg. # of SNPs in an individual classifier: 21.60
        (sd: 6.47, min: 14, max: 36, median: 20.00)
    avg. # of haplotypes in an individual classifier: 56.50
        (sd: 18.08, min: 26, max: 77, median: 61.00)
    avg. out-of-bag accuracy: 85.60%
        (sd: 9.22%, min: 66.67%, max: 96.15%, median: 90.10%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0003038237 0.0003220623 0.0004862091 0.0018325695 0.0037925315 0.0087410133 
        Max.         Mean           SD 
0.0940510882 0.0104030527 0.0181801883 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 354 SNPs, 17 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 24
Predicting (2019-04-13 03:03:53)	0%
Predicting (2019-04-13 03:03:53)	100%
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 24
# of unique HLA alleles: 14
# of unique HLA genotypes: 16
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
 4 (16.7%)  3 (12.5%)  6 (25.0%) 11 (45.8%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.000e+00 7.431e-05 5.956e-04 4.933e-03 4.212e-03 3.363e-02 
  total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1            24          18            42    0.75     0.875              0
  n.call call.rate
1     24         1


Exclude 6 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 343, # of samples: 31
# of unique HLA alleles: 7
[-] 2019-04-13 03:03:53
[1] 2019-04-13 03:03:53, OOB Acc: 86.36%, # of SNPs: 10, # of Haplo: 19
[2] 2019-04-13 03:03:53, OOB Acc: 92.31%, # of SNPs: 12, # of Haplo: 37
[3] 2019-04-13 03:03:53, OOB Acc: 86.36%, # of SNPs: 12, # of Haplo: 21
[4] 2019-04-13 03:03:53, OOB Acc: 95.00%, # of SNPs: 10, # of Haplo: 17
[5] 2019-04-13 03:03:53, OOB Acc: 88.89%, # of SNPs: 11, # of Haplo: 31
[6] 2019-04-13 03:03:53, OOB Acc: 100.00%, # of SNPs: 14, # of Haplo: 41
[7] 2019-04-13 03:03:53, OOB Acc: 90.00%, # of SNPs: 9, # of Haplo: 16
[8] 2019-04-13 03:03:53, OOB Acc: 95.45%, # of SNPs: 10, # of Haplo: 10
[9] 2019-04-13 03:03:53, OOB Acc: 83.33%, # of SNPs: 7, # of Haplo: 9
[10] 2019-04-13 03:03:53, OOB Acc: 87.50%, # of SNPs: 7, # of Haplo: 17
Calculating matching proportion:
       Min.    0.1% Qu.      1% Qu.     1st Qu.      Median     3rd Qu. 
0.002003247 0.002016202 0.002132800 0.009820132 0.018803876 0.040262061 
       Max.        Mean          SD 
0.496473774 0.061923149 0.128202241 
Accuracy with training data: 96.8%
Out-of-bag accuracy: 90.5%
Gene: DQA1
Training dataset: 31 samples X 343 SNPs
    # of HLA alleles: 7
    # of individual classifiers: 10
    total # of SNPs used: 73
    avg. # of SNPs in an individual classifier: 10.20
        (sd: 2.20, min: 7, max: 14, median: 10.00)
    avg. # of haplotypes in an individual classifier: 21.80
        (sd: 10.93, min: 9, max: 41, median: 18.00)
    avg. out-of-bag accuracy: 90.52%
        (sd: 5.12%, min: 83.33%, max: 100.00%, median: 89.44%)
Matching proportion:
       Min.    0.1% Qu.      1% Qu.     1st Qu.      Median     3rd Qu. 
0.002003247 0.002016202 0.002132800 0.009820132 0.018803876 0.040262061 
       Max.        Mean          SD 
0.496473774 0.061923149 0.128202241 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 343 SNPs, 7 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 29
Predicting (2019-04-13 03:03:53)	0%
Predicting (2019-04-13 03:03:53)	100%
Gene: DQA1
Range: [32605169bp, 32612152bp] on hg19
# of samples: 29
# of unique HLA alleles: 7
# of unique HLA genotypes: 16
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
 3 (10.3%)  5 (17.2%)  5 (17.2%) 16 (55.2%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0007125 0.0034787 0.0080790 0.0290830 0.0159372 0.4975405 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            29          23            51 0.7931034 0.8793103              0
  n.call call.rate
1     29         1


Exclude 5 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 19
# of SNPs: 351, # of samples: 34
# of unique HLA alleles: 12
[-] 2019-04-13 03:03:53
[1] 2019-04-13 03:03:53, OOB Acc: 81.82%, # of SNPs: 17, # of Haplo: 58
[2] 2019-04-13 03:03:53, OOB Acc: 80.77%, # of SNPs: 14, # of Haplo: 42
[3] 2019-04-13 03:03:53, OOB Acc: 76.67%, # of SNPs: 11, # of Haplo: 23
[4] 2019-04-13 03:03:53, OOB Acc: 78.57%, # of SNPs: 14, # of Haplo: 16
[5] 2019-04-13 03:03:53, OOB Acc: 89.29%, # of SNPs: 16, # of Haplo: 37
[6] 2019-04-13 03:03:54, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 40
[7] 2019-04-13 03:03:54, OOB Acc: 88.46%, # of SNPs: 14, # of Haplo: 26
[8] 2019-04-13 03:03:54, OOB Acc: 92.31%, # of SNPs: 16, # of Haplo: 35
[9] 2019-04-13 03:03:54, OOB Acc: 87.50%, # of SNPs: 11, # of Haplo: 70
[10] 2019-04-13 03:03:54, OOB Acc: 80.00%, # of SNPs: 10, # of Haplo: 27
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004974378 0.0005193302 0.0007163617 0.0045545694 0.0089462549 0.0282489093 
        Max.         Mean           SD 
0.5204994077 0.0604813618 0.1368041942 
Accuracy with training data: 94.1%
Out-of-bag accuracy: 84.6%
Gene: DQB1
Training dataset: 34 samples X 351 SNPs
    # of HLA alleles: 12
    # of individual classifiers: 10
    total # of SNPs used: 89
    avg. # of SNPs in an individual classifier: 13.70
        (sd: 2.36, min: 10, max: 17, median: 14.00)
    avg. # of haplotypes in an individual classifier: 37.40
        (sd: 16.41, min: 16, max: 70, median: 36.00)
    avg. out-of-bag accuracy: 84.63%
        (sd: 5.65%, min: 76.67%, max: 92.31%, median: 84.66%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004974378 0.0005193302 0.0007163617 0.0045545694 0.0089462549 0.0282489093 
        Max.         Mean           SD 
0.5204994077 0.0604813618 0.1368041942 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 351 SNPs, 12 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:54)	0%
Predicting (2019-04-13 03:03:54)	100%
Gene: DQB1
Range: [32627241bp, 32634466bp] on hg19
# of samples: 26
# of unique HLA alleles: 10
# of unique HLA genotypes: 16
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.8%)  5 (19.2%)  6 (23.1%) 14 (53.8%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
2.100e-08 7.787e-04 4.277e-03 7.451e-03 1.142e-02 2.731e-02 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            26          22            46 0.8461538 0.8846154              0
  n.call call.rate
1     26         1


Exclude 4 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 18
# of SNPs: 323, # of samples: 35
# of unique HLA alleles: 20
[-] 2019-04-13 03:03:54
[1] 2019-04-13 03:03:54, OOB Acc: 70.00%, # of SNPs: 19, # of Haplo: 83
[2] 2019-04-13 03:03:54, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 59
[3] 2019-04-13 03:03:55, OOB Acc: 79.17%, # of SNPs: 14, # of Haplo: 68
[4] 2019-04-13 03:03:55, OOB Acc: 86.36%, # of SNPs: 18, # of Haplo: 57
[5] 2019-04-13 03:03:55, OOB Acc: 75.00%, # of SNPs: 16, # of Haplo: 56
[6] 2019-04-13 03:03:55, OOB Acc: 75.00%, # of SNPs: 19, # of Haplo: 152
[7] 2019-04-13 03:03:56, OOB Acc: 69.23%, # of SNPs: 20, # of Haplo: 129
[8] 2019-04-13 03:03:56, OOB Acc: 71.88%, # of SNPs: 16, # of Haplo: 61
[9] 2019-04-13 03:03:56, OOB Acc: 69.23%, # of SNPs: 24, # of Haplo: 65
[10] 2019-04-13 03:03:56, OOB Acc: 80.00%, # of SNPs: 28, # of Haplo: 117
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0006122513 0.0006129451 0.0006191888 0.0015993856 0.0024553804 0.0054592144 
        Max.         Mean           SD 
0.5551955860 0.0342140882 0.1257480491 
Accuracy with training data: 95.7%
Out-of-bag accuracy: 76.3%
Gene: DRB1
Training dataset: 35 samples X 323 SNPs
    # of HLA alleles: 20
    # of individual classifiers: 10
    total # of SNPs used: 137
    avg. # of SNPs in an individual classifier: 18.90
        (sd: 4.31, min: 14, max: 28, median: 18.50)
    avg. # of haplotypes in an individual classifier: 84.70
        (sd: 34.99, min: 56, max: 152, median: 66.50)
    avg. out-of-bag accuracy: 76.34%
        (sd: 6.76%, min: 69.23%, max: 87.50%, median: 75.00%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0006122513 0.0006129451 0.0006191888 0.0015993856 0.0024553804 0.0054592144 
        Max.         Mean           SD 
0.5551955860 0.0342140882 0.1257480491 
Genome assembly: hg19
HIBAG model: 10 individual classifiers, 323 SNPs, 20 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 25
Predicting (2019-04-13 03:03:56)	0%
Predicting (2019-04-13 03:03:56)	100%
Gene: DRB1
Range: [32546546bp, 32557613bp] on hg19
# of samples: 25
# of unique HLA alleles: 9
# of unique HLA genotypes: 17
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
 6 (24.0%)  6 (24.0%)  7 (28.0%)  6 (24.0%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0000000 0.0003357 0.0334829 0.0008981 0.5581343 
  total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1            25          16            39    0.64      0.78              0
  n.call call.rate
1     25         1


> 
> 
> 
> #############################################################
> 
> {
+ 	function.list <- readRDS(
+ 		system.file("Meta", "Rd.rds", package="HIBAG"))$Name
+ 
+ 	sapply(function.list, FUN = function(func.name)
+ 		{
+ 			args <- list(
+ 				topic   = func.name,
+ 				package = "HIBAG",
+ 				echo = FALSE,
+ 				verbose = FALSE,
+ 				ask = FALSE
+ 			)
+ 			suppressWarnings(do.call(example, args))
+ 			NULL
+ 		})
+ 	invisible()
+ }
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:56
     1, SNP: 155, Loss: 186.198, OOB Acc: 68.18%, # of Haplo: 13
     2, SNP: 111, Loss: 138.902, OOB Acc: 72.73%, # of Haplo: 13
     3, SNP: 159, Loss: 134.624, OOB Acc: 81.82%, # of Haplo: 15
     4, SNP: 75, Loss: 120.53, OOB Acc: 86.36%, # of Haplo: 16
     5, SNP: 129, Loss: 79.3436, OOB Acc: 90.91%, # of Haplo: 20
     6, SNP: 135, Loss: 56.5856, OOB Acc: 90.91%, # of Haplo: 20
     7, SNP: 162, Loss: 40.1347, OOB Acc: 90.91%, # of Haplo: 20
     8, SNP: 195, Loss: 29.7565, OOB Acc: 90.91%, # of Haplo: 24
     9, SNP: 182, Loss: 23.1586, OOB Acc: 90.91%, # of Haplo: 24
    10, SNP: 24, Loss: 16.7007, OOB Acc: 90.91%, # of Haplo: 27
    11, SNP: 154, Loss: 15.6368, OOB Acc: 90.91%, # of Haplo: 28
    12, SNP: 17, Loss: 15.4059, OOB Acc: 90.91%, # of Haplo: 39
    13, SNP: 199, Loss: 14.4894, OOB Acc: 90.91%, # of Haplo: 40
    14, SNP: 73, Loss: 8.03154, OOB Acc: 90.91%, # of Haplo: 42
    15, SNP: 151, Loss: 5.75619, OOB Acc: 90.91%, # of Haplo: 42
[1] 2019-04-13 03:03:57, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
     1, SNP: 87, Loss: 163.788, OOB Acc: 50.00%, # of Haplo: 15
     2, SNP: 179, Loss: 159.036, OOB Acc: 57.69%, # of Haplo: 16
     3, SNP: 162, Loss: 127.744, OOB Acc: 61.54%, # of Haplo: 17
     4, SNP: 113, Loss: 120.348, OOB Acc: 76.92%, # of Haplo: 17
     5, SNP: 149, Loss: 99.7027, OOB Acc: 80.77%, # of Haplo: 19
     6, SNP: 183, Loss: 63.9201, OOB Acc: 80.77%, # of Haplo: 19
     7, SNP: 82, Loss: 46.9454, OOB Acc: 80.77%, # of Haplo: 19
     8, SNP: 73, Loss: 36.2707, OOB Acc: 84.62%, # of Haplo: 21
     9, SNP: 47, Loss: 34.9876, OOB Acc: 84.62%, # of Haplo: 22
    10, SNP: 140, Loss: 27.9494, OOB Acc: 84.62%, # of Haplo: 22
    11, SNP: 189, Loss: 23.9141, OOB Acc: 84.62%, # of Haplo: 23
    12, SNP: 249, Loss: 18.884, OOB Acc: 84.62%, # of Haplo: 27
    13, SNP: 151, Loss: 15.8511, OOB Acc: 84.62%, # of Haplo: 27
    14, SNP: 21, Loss: 11.1982, OOB Acc: 88.46%, # of Haplo: 30
    15, SNP: 215, Loss: 10.7762, OOB Acc: 88.46%, # of Haplo: 32
    16, SNP: 136, Loss: 10.5472, OOB Acc: 88.46%, # of Haplo: 38
    17, SNP: 75, Loss: 10.4276, OOB Acc: 88.46%, # of Haplo: 49
    18, SNP: 28, Loss: 10.3043, OOB Acc: 88.46%, # of Haplo: 55
    19, SNP: 266, Loss: 10.3043, OOB Acc: 92.31%, # of Haplo: 48
    20, SNP: 199, Loss: 9.5327, OOB Acc: 92.31%, # of Haplo: 48
[2] 2019-04-13 03:03:57, OOB Acc: 92.31%, # of SNPs: 20, # of Haplo: 48
     1, SNP: 167, Loss: 182.217, OOB Acc: 55.88%, # of Haplo: 14
     2, SNP: 126, Loss: 120.313, OOB Acc: 64.71%, # of Haplo: 16
     3, SNP: 135, Loss: 94.3559, OOB Acc: 73.53%, # of Haplo: 19
     4, SNP: 173, Loss: 73.5379, OOB Acc: 79.41%, # of Haplo: 19
     5, SNP: 196, Loss: 47.4931, OOB Acc: 85.29%, # of Haplo: 20
     6, SNP: 124, Loss: 28.5275, OOB Acc: 88.24%, # of Haplo: 20
     7, SNP: 112, Loss: 27.3051, OOB Acc: 88.24%, # of Haplo: 22
     8, SNP: 150, Loss: 26.8966, OOB Acc: 88.24%, # of Haplo: 22
     9, SNP: 106, Loss: 20.7953, OOB Acc: 88.24%, # of Haplo: 21
    10, SNP: 116, Loss: 20.4753, OOB Acc: 88.24%, # of Haplo: 21
    11, SNP: 16, Loss: 20.2453, OOB Acc: 88.24%, # of Haplo: 24
    12, SNP: 6, Loss: 20.1953, OOB Acc: 88.24%, # of Haplo: 28
    13, SNP: 181, Loss: 19.9842, OOB Acc: 88.24%, # of Haplo: 28
    14, SNP: 73, Loss: 11.7972, OOB Acc: 91.18%, # of Haplo: 33
    15, SNP: 96, Loss: 11.7743, OOB Acc: 91.18%, # of Haplo: 37
    16, SNP: 177, Loss: 11.7597, OOB Acc: 91.18%, # of Haplo: 45
    17, SNP: 151, Loss: 11.3816, OOB Acc: 91.18%, # of Haplo: 45
    18, SNP: 199, Loss: 10.1586, OOB Acc: 91.18%, # of Haplo: 45
[3] 2019-04-13 03:03:57, OOB Acc: 91.18%, # of SNPs: 18, # of Haplo: 45
     1, SNP: 191, Loss: 189.049, OOB Acc: 63.64%, # of Haplo: 15
     2, SNP: 100, Loss: 160.313, OOB Acc: 72.73%, # of Haplo: 18
     3, SNP: 158, Loss: 135.403, OOB Acc: 77.27%, # of Haplo: 19
     4, SNP: 86, Loss: 112.795, OOB Acc: 86.36%, # of Haplo: 22
     5, SNP: 134, Loss: 75.8819, OOB Acc: 86.36%, # of Haplo: 24
     6, SNP: 85, Loss: 54.3124, OOB Acc: 90.91%, # of Haplo: 29
     7, SNP: 128, Loss: 36.9547, OOB Acc: 90.91%, # of Haplo: 29
     8, SNP: 77, Loss: 30.0981, OOB Acc: 90.91%, # of Haplo: 32
     9, SNP: 54, Loss: 24.7805, OOB Acc: 90.91%, # of Haplo: 32
    10, SNP: 21, Loss: 20.945, OOB Acc: 90.91%, # of Haplo: 36
    11, SNP: 199, Loss: 20.2197, OOB Acc: 90.91%, # of Haplo: 36
    12, SNP: 266, Loss: 19.48, OOB Acc: 90.91%, # of Haplo: 38
    13, SNP: 52, Loss: 19.1914, OOB Acc: 90.91%, # of Haplo: 38
    14, SNP: 215, Loss: 18.9375, OOB Acc: 90.91%, # of Haplo: 38
    15, SNP: 73, Loss: 14.4966, OOB Acc: 90.91%, # of Haplo: 38
    16, SNP: 151, Loss: 13.3459, OOB Acc: 90.91%, # of Haplo: 39
[4] 2019-04-13 03:03:57, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 39
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0001216782 0.0001372891 0.0002777870 0.0023012412 0.0072873069 0.0234246874 
        Max.         Mean           SD 
0.2886695022 0.0296925310 0.0666389121 
Accuracy with training data: 98.5%
Out-of-bag accuracy: 91.3%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 54
    avg. # of SNPs in an individual classifier: 17.25
        (sd: 2.22, min: 15, max: 20, median: 17.00)
    avg. # of haplotypes in an individual classifier: 43.50
        (sd: 3.87, min: 39, max: 48, median: 43.50)
    avg. out-of-bag accuracy: 91.33%
        (sd: 0.67%, min: 90.91%, max: 92.31%, median: 91.04%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0001216782 0.0001372891 0.0002777870 0.0023012412 0.0072873069 0.0234246874 
        Max.         Mean           SD 
0.2886695022 0.0296925310 0.0666389121 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:57)	0%
Predicting (2019-04-13 03:03:57)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.8%)   2 (7.7%)  3 (11.5%) 20 (76.9%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0007878 0.0047578 0.0193435 0.0128352 0.2886695 
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:57)	0%
Predicting (2019-04-13 03:03:57)	100%
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 90
Predicting (2019-04-13 03:03:57)	0%
Predicting (2019-04-13 03:03:57)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
using the default genome assembly (assembly="hg19")
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 12
# of unique HLA genotypes: 28
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 100
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
dominant model:
      [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p
24:02    49        11    42.9        81.8   4.0074  0.045*   0.042*
-----                                                              
01:01    36        24    50.0        50.0   0.0000  1.000    1.000 
02:01    25        35    52.0        48.6   0.0000  1.000    1.000 
02:06    59         1    50.8         0.0   0.0000  1.000    1.000 
03:01    51         9    49.0        55.6   0.0000  1.000    1.000 
11:01    55         5    50.9        40.0   0.0000  1.000    1.000 
23:01    58         2    50.0        50.0   0.0000  1.000    1.000 
24:03    59         1    50.8         0.0   0.0000  1.000    1.000 
25:01    55         5    52.7        20.0   0.8727  0.350    0.353 
26:01    57         3    52.6         0.0   1.4035  0.236    0.237 
29:02    56         4    51.8        25.0   0.2679  0.605    0.612 
31:01    57         3    49.1        66.7   0.0000  1.000    1.000 
32:01    56         4    46.4       100.0   2.4107  0.121    0.112 
68:01    57         3    52.6         0.0   1.4035  0.236    0.237 
additive model:
      [-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p
01:01  95  25  50.5  48.0   0.0000  1.000    1.000 
02:01  77  43  48.1  53.5   0.1450  0.703    0.704 
02:06 119   1  50.4   0.0   0.0000  1.000    1.000 
03:01 111   9  49.5  55.6   0.0000  1.000    1.000 
11:01 115   5  50.4  40.0   0.0000  1.000    1.000 
23:01 117   3  50.4  33.3   0.0000  1.000    1.000 
24:02 109  11  46.8  81.8   3.6030  0.058    0.053 
24:03 119   1  50.4   0.0   0.0000  1.000    1.000 
25:01 115   5  51.3  20.0   0.8348  0.361    0.364 
26:01 117   3  51.3   0.0   1.3675  0.242    0.244 
29:02 116   4  50.9  25.0   0.2586  0.611    0.619 
31:01 117   3  49.6  66.7   0.0000  1.000    1.000 
32:01 116   4  48.3 100.0   2.3276  0.127    0.119 
68:01 117   3  51.3   0.0   1.3675  0.242    0.244 
recessive model:
      [-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p
01:01        59     1        50.8       0    0.000  1.000    1.000 
02:01        52     8        46.2      75    1.298  0.255    0.254 
02:06        60     0        50.0       .        .       .        .
03:01        60     0        50.0       .        .       .        .
11:01        60     0        50.0       .        .       .        .
23:01        59     1        50.8       0    0.000  1.000    1.000 
24:02        60     0        50.0       .        .       .        .
24:03        60     0        50.0       .        .       .        .
25:01        60     0        50.0       .        .       .        .
26:01        60     0        50.0       .        .       .        .
29:02        60     0        50.0       .        .       .        .
31:01        60     0        50.0       .        .       .        .
32:01        60     0        50.0       .        .       .        .
68:01        60     0        50.0       .        .       .        .
genotype model:
      [-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02    49    11     0    42.9    81.8       .   4.0074  0.045*   0.042*
-----                                                                    
01:01    36    23     1    50.0    52.2       0   1.0435  0.593    1.000 
02:01    25    27     8    52.0    40.7      75   2.9659  0.227    0.271 
02:06    59     1     0    50.8     0.0       .   0.0000  1.000    1.000 
03:01    51     9     0    49.0    55.6       .   0.0000  1.000    1.000 
11:01    55     5     0    50.9    40.0       .   0.0000  1.000    1.000 
23:01    58     1     1    50.0   100.0       0   2.0000  0.368    1.000 
24:03    59     1     0    50.8     0.0       .   0.0000  1.000    1.000 
25:01    55     5     0    52.7    20.0       .   0.8727  0.350    0.353 
26:01    57     3     0    52.6     0.0       .   1.4035  0.236    0.237 
29:02    56     4     0    51.8    25.0       .   0.2679  0.605    0.612 
31:01    57     3     0    49.1    66.7       .   0.0000  1.000    1.000 
32:01    56     4     0    46.4   100.0       .   2.4107  0.121    0.112 
68:01    57     3     0    52.6     0.0       .   1.4035  0.236    0.237 
dominant model:
      [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p
01:01    36        24  -0.14684     -0.117427  0.909 
02:01    25        35  -0.32331     -0.000618  0.190 
02:06    59         1  -0.14024      0.170057       .
03:01    51         9  -0.05600     -0.583178  0.147 
11:01    55         5  -0.19188      0.489815  0.287 
23:01    58         2  -0.15400      0.413687  0.281 
24:02    49        11  -0.10486     -0.269664  0.537 
24:03    59         1  -0.11409     -1.373118       .
25:01    55         5  -0.12237     -0.274749  0.742 
26:01    57         3  -0.12473     -0.331558  0.690 
29:02    56         4  -0.13044     -0.199941  0.789 
31:01    57         3  -0.10097     -0.783003  0.607 
32:01    56         4  -0.07702     -0.947791  0.092 
68:01    57         3  -0.16915      0.512457  0.196 
genotype model:
      [-/-] [-/h] [h/h] avg.[-/-] avg.[-/h] avg.[h/h] anova.p
01:01    36    23     1  -0.14684  -0.08833  -0.78655  0.784 
02:01    25    27     8  -0.32331  -0.02341   0.07631  0.446 
02:06    59     1     0  -0.14024   0.17006         .  0.756 
03:01    51     9     0  -0.05600  -0.58318         .  0.138 
11:01    55     5     0  -0.19188   0.48981         .  0.137 
23:01    58     1     1  -0.15400   0.10762   0.71975  0.663 
24:02    49    11     0  -0.10486  -0.26966         .  0.618 
24:03    59     1     0  -0.11409  -1.37312         .  0.205 
25:01    55     5     0  -0.12237  -0.27475         .  0.742 
26:01    57     3     0  -0.12473  -0.33156         .  0.725 
29:02    56     4     0  -0.13044  -0.19994         .  0.892 
31:01    57     3     0  -0.10097  -0.78300         .  0.243 
32:01    56     4     0  -0.07702  -0.94779         .  0.086 
68:01    57     3     0  -0.16915   0.51246         .  0.243 
Logistic regression (dominant model) with 60 individuals:
  glm(case ˜ h, family = binomial, data = data)
      [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p      h.est
24:02    49        11    42.9        81.8   4.0074  0.045*   0.042*  1.792e+00
-----                                                                         
01:01    36        24    50.0        50.0   0.0000  1.000    1.000  -8.777e-16
02:01    25        35    52.0        48.6   0.0000  1.000    1.000  -1.372e-01
02:06    59         1    50.8         0.0   0.0000  1.000    1.000  -1.560e+01
03:01    51         9    49.0        55.6   0.0000  1.000    1.000   2.624e-01
11:01    55         5    50.9        40.0   0.0000  1.000    1.000  -4.418e-01
23:01    58         2    50.0        50.0   0.0000  1.000    1.000   2.874e-15
24:03    59         1    50.8         0.0   0.0000  1.000    1.000  -1.560e+01
25:01    55         5    52.7        20.0   0.8727  0.350    0.353  -1.495e+00
26:01    57         3    52.6         0.0   1.4035  0.236    0.237  -1.667e+01
29:02    56         4    51.8        25.0   0.2679  0.605    0.612  -1.170e+00
31:01    57         3    49.1        66.7   0.0000  1.000    1.000   7.282e-01
32:01    56         4    46.4       100.0   2.4107  0.121    0.112   1.771e+01
68:01    57         3    52.6         0.0   1.4035  0.236    0.237  -1.667e+01
          h.2.5%   h.97.5% h.pval
24:02     0.1585    3.4251 0.032*
-----                            
01:01    -1.0330    1.0330 1.000 
02:01    -1.1643    0.8899 0.793 
02:06 -2868.1268 2836.9268 0.991 
03:01    -1.1624    1.6872 0.718 
11:01    -2.3074    1.4237 0.643 
23:01    -2.8192    2.8192 1.000 
24:03 -2868.1268 2836.9268 0.991 
25:01    -3.7498    0.7588 0.194 
26:01 -2731.9621 2698.6192 0.990 
29:02    -3.4931    1.1530 0.324 
31:01    -1.7277    3.1842 0.561 
32:01 -3859.2763 3894.6947 0.993 
68:01 -2731.9621 2698.6192 0.990 
Logistic regression (dominant model) with 60 individuals:
  glm(case ˜ h + pc1, family = binomial, data = data)
      [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p      h.est
24:02    49        11    42.9        81.8   4.0074  0.045*   0.042*  1.793e+00
-----                                                                         
01:01    36        24    50.0        50.0   0.0000  1.000    1.000  -2.268e-04
02:01    25        35    52.0        48.6   0.0000  1.000    1.000  -1.370e-01
02:06    59         1    50.8         0.0   0.0000  1.000    1.000  -1.562e+01
03:01    51         9    49.0        55.6   0.0000  1.000    1.000   2.686e-01
11:01    55         5    50.9        40.0   0.0000  1.000    1.000  -4.451e-01
23:01    58         2    50.0        50.0   0.0000  1.000    1.000  -3.062e-03
24:03    59         1    50.8         0.0   0.0000  1.000    1.000  -1.560e+01
25:01    55         5    52.7        20.0   0.8727  0.350    0.353  -1.501e+00
26:01    57         3    52.6         0.0   1.4035  0.236    0.237  -1.667e+01
29:02    56         4    51.8        25.0   0.2679  0.605    0.612  -1.189e+00
31:01    57         3    49.1        66.7   0.0000  1.000    1.000   7.289e-01
32:01    56         4    46.4       100.0   2.4107  0.121    0.112   1.781e+01
68:01    57         3    52.6         0.0   1.4035  0.236    0.237  -1.673e+01
          h.2.5%   h.97.5% h.pval   pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02     0.1587    3.4264 0.032*  0.011111  -0.5249    0.5471   0.968 
-----                                                                  
01:01    -1.0334    1.0330 1.000  -0.005807  -0.5126    0.5010   0.982 
02:01    -1.1652    0.8913 0.794  -0.002618  -0.5102    0.5049   0.992 
02:06 -2868.1460 2836.9076 0.991  -0.028534  -0.5374    0.4803   0.912 
03:01    -1.1813    1.7185 0.717   0.011958  -0.5044    0.5283   0.964 
11:01    -2.3225    1.4322 0.642   0.008025  -0.5026    0.5186   0.975 
23:01    -2.8348    2.8287 0.998  -0.005857  -0.5148    0.5031   0.982 
24:03 -2868.1286 2836.9250 0.991  -0.011249  -0.5182    0.4957   0.965 
25:01    -3.7579    0.7568 0.193  -0.025685  -0.5490    0.4976   0.923 
26:01 -2731.8901 2698.5450 0.990  -0.014069  -0.5297    0.5015   0.957 
29:02    -3.5309    1.1526 0.320   0.033234  -0.4796    0.5461   0.899 
31:01    -1.7274    3.1851 0.561  -0.008320  -0.5153    0.4987   0.974 
32:01 -3845.6317 3881.2510 0.993  -0.125426  -0.6671    0.4162   0.650 
68:01 -2721.2124 2687.7497 0.990  -0.086589  -0.6512    0.4781   0.764 
Logistic regression (dominant model) with 60 individuals:
  glm(case ˜ h + pc1, family = binomial, data = data)
      [-/-] [-/h,h/h] %.[-/-] %.[-/h,h/h] chisq.st chisq.p fisher.p  h.est_OR
24:02    49        11    42.9        81.8   4.0074  0.045*   0.042* 6.005e+00
-----                                                                        
01:01    36        24    50.0        50.0   0.0000  1.000    1.000  9.998e-01
02:01    25        35    52.0        48.6   0.0000  1.000    1.000  8.720e-01
02:06    59         1    50.8         0.0   0.0000  1.000    1.000  1.647e-07
03:01    51         9    49.0        55.6   0.0000  1.000    1.000  1.308e+00
11:01    55         5    50.9        40.0   0.0000  1.000    1.000  6.407e-01
23:01    58         2    50.0        50.0   0.0000  1.000    1.000  9.969e-01
24:03    59         1    50.8         0.0   0.0000  1.000    1.000  1.676e-07
25:01    55         5    52.7        20.0   0.8727  0.350    0.353  2.230e-01
26:01    57         3    52.6         0.0   1.4035  0.236    0.237  5.744e-08
29:02    56         4    51.8        25.0   0.2679  0.605    0.612  3.045e-01
31:01    57         3    49.1        66.7   0.0000  1.000    1.000  2.073e+00
32:01    56         4    46.4       100.0   2.4107  0.121    0.112  5.428e+07
68:01    57         3    52.6         0.0   1.4035  0.236    0.237  5.416e-08
      h.2.5%_OR h.97.5%_OR h.pval   pc1.est pc1.2.5% pc1.97.5% pc1.pval
24:02   1.17200     30.766 0.032*  0.011111  -0.5249    0.5471   0.968 
-----                                                                  
01:01   0.35579      2.809 1.000  -0.005807  -0.5126    0.5010   0.982 
02:01   0.31185      2.438 0.794  -0.002618  -0.5102    0.5049   0.992 
02:06   0.00000        Inf 0.991  -0.028534  -0.5374    0.4803   0.912 
03:01   0.30687      5.576 0.717   0.011958  -0.5044    0.5283   0.964 
11:01   0.09803      4.188 0.642   0.008025  -0.5026    0.5186   0.975 
23:01   0.05873     16.923 0.998  -0.005857  -0.5148    0.5031   0.982 
24:03   0.00000        Inf 0.991  -0.011249  -0.5182    0.4957   0.965 
25:01   0.02333      2.131 0.193  -0.025685  -0.5490    0.4976   0.923 
26:01   0.00000        Inf 0.990  -0.014069  -0.5297    0.5015   0.957 
29:02   0.02928      3.167 0.320   0.033234  -0.4796    0.5461   0.899 
31:01   0.17774     24.171 0.561  -0.008320  -0.5153    0.4987   0.974 
32:01   0.00000        Inf 0.993  -0.125426  -0.6671    0.4162   0.650 
68:01   0.00000        Inf 0.990  -0.086589  -0.6512    0.4781   0.764 
Linear regression (dominant model) with 60 individuals:
  glm(y ˜ h, data = data)
      [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p    h.est  h.2.5% h.97.5%
01:01    36        24  -0.14684     -0.117427  0.909   0.02941 -0.4805  0.5393
02:01    25        35  -0.32331     -0.000618  0.190   0.32269 -0.1772  0.8226
02:06    59         1  -0.14024      0.170057       .  0.31030 -1.6397  2.2603
03:01    51         9  -0.05600     -0.583178  0.147  -0.52718 -1.2136  0.1592
11:01    55         5  -0.19188      0.489815  0.287   0.68170 -0.2051  1.5685
23:01    58         2  -0.15400      0.413687  0.281   0.56768 -0.8165  1.9518
24:02    49        11  -0.10486     -0.269664  0.537  -0.16481 -0.8091  0.4795
24:03    59         1  -0.11409     -1.373118       . -1.25903 -3.1835  0.6655
25:01    55         5  -0.12237     -0.274749  0.742  -0.15237 -1.0555  0.7507
26:01    57         3  -0.12473     -0.331558  0.690  -0.20683 -1.3519  0.9383
29:02    56         4  -0.13044     -0.199941  0.789  -0.06950 -1.0709  0.9319
31:01    57         3  -0.10097     -0.783003  0.607  -0.68203 -1.8149  0.4508
32:01    56         4  -0.07702     -0.947791  0.092  -0.87077 -1.8470  0.1054
68:01    57         3  -0.16915      0.512457  0.196   0.68161 -0.4512  1.8145
      h.pval
01:01 0.910 
02:01 0.211 
02:06 0.756 
03:01 0.138 
11:01 0.137 
23:01 0.425 
24:02 0.618 
24:03 0.205 
25:01 0.742 
26:01 0.725 
29:02 0.892 
31:01 0.243 
32:01 0.086 
68:01 0.243 
Linear regression (dominant model) with 60 individuals:
  glm(y ˜ h + pc1, data = data)
      [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p    h.est  h.2.5%
01:01    36        24  -0.14684     -0.117427  0.909   0.03377 -0.4773
02:01    25        35  -0.32331     -0.000618  0.190   0.31273 -0.1891
02:06    59         1  -0.14024      0.170057       .  0.38821 -1.5722
03:01    51         9  -0.05600     -0.583178  0.147  -0.48613 -1.1884
11:01    55         5  -0.19188      0.489815  0.287   0.64430 -0.2520
23:01    58         2  -0.15400      0.413687  0.281   0.63150 -0.7598
24:02    49        11  -0.10486     -0.269664  0.537  -0.15742 -0.8034
24:03    59         1  -0.11409     -1.373118       . -1.24145 -3.1708
25:01    55         5  -0.12237     -0.274749  0.742  -0.13241 -1.0388
26:01    57         3  -0.12473     -0.331558  0.690  -0.19823 -1.3460
29:02    56         4  -0.13044     -0.199941  0.789  -0.13606 -1.1496
31:01    57         3  -0.10097     -0.783003  0.607  -0.69057 -1.8254
32:01    56         4  -0.07702     -0.947791  0.092  -0.99595 -1.9862
68:01    57         3  -0.16915      0.512457  0.196   0.76795 -0.3749
        h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01  0.544844 0.897  0.11172  -0.1390    0.3624   0.386 
02:01  0.814606 0.227  0.10412  -0.1436    0.3519   0.414 
02:06  2.348616 0.699  0.11570  -0.1356    0.3670   0.371 
03:01  0.216142 0.180  0.07919  -0.1719    0.3303   0.539 
11:01  1.540569 0.164  0.09117  -0.1569    0.3392   0.474 
23:01  2.022811 0.377  0.12207  -0.1280    0.3721   0.343 
24:02  0.488543 0.635  0.10982  -0.1404    0.3601   0.393 
24:03  0.687920 0.212  0.10809  -0.1392    0.3554   0.395 
25:01  0.773943 0.776  0.10956  -0.1413    0.3604   0.396 
26:01  0.949529 0.736  0.11067  -0.1398    0.3611   0.390 
29:02  0.877431 0.793  0.11626  -0.1369    0.3694   0.372 
31:01  0.444260 0.238  0.11387  -0.1338    0.3615   0.371 
32:01 -0.005739 0.054  0.16001  -0.0873    0.4073   0.210 
68:01  1.910822 0.193  0.13482  -0.1146    0.3842   0.294 
Linear regression (dominant model) with 60 individuals:
  glm(y ˜ h + pc1, data = data)
      [-/-] [-/h,h/h] avg.[-/-] avg.[-/h,h/h] ttest.p    h.est  h.2.5%
01:01    36        24  -0.14684     -0.117427  0.909   0.03377 -0.4773
02:01    25        35  -0.32331     -0.000618  0.190   0.31273 -0.1891
02:06    59         1  -0.14024      0.170057       .  0.38821 -1.5722
03:01    51         9  -0.05600     -0.583178  0.147  -0.48613 -1.1884
11:01    55         5  -0.19188      0.489815  0.287   0.64430 -0.2520
23:01    58         2  -0.15400      0.413687  0.281   0.63150 -0.7598
24:02    49        11  -0.10486     -0.269664  0.537  -0.15742 -0.8034
24:03    59         1  -0.11409     -1.373118       . -1.24145 -3.1708
25:01    55         5  -0.12237     -0.274749  0.742  -0.13241 -1.0388
26:01    57         3  -0.12473     -0.331558  0.690  -0.19823 -1.3460
29:02    56         4  -0.13044     -0.199941  0.789  -0.13606 -1.1496
31:01    57         3  -0.10097     -0.783003  0.607  -0.69057 -1.8254
32:01    56         4  -0.07702     -0.947791  0.092  -0.99595 -1.9862
68:01    57         3  -0.16915      0.512457  0.196   0.76795 -0.3749
        h.97.5% h.pval pc1.est pc1.2.5% pc1.97.5% pc1.pval
01:01  0.544844 0.897  0.11172  -0.1390    0.3624   0.386 
02:01  0.814606 0.227  0.10412  -0.1436    0.3519   0.414 
02:06  2.348616 0.699  0.11570  -0.1356    0.3670   0.371 
03:01  0.216142 0.180  0.07919  -0.1719    0.3303   0.539 
11:01  1.540569 0.164  0.09117  -0.1569    0.3392   0.474 
23:01  2.022811 0.377  0.12207  -0.1280    0.3721   0.343 
24:02  0.488543 0.635  0.10982  -0.1404    0.3601   0.393 
24:03  0.687920 0.212  0.10809  -0.1392    0.3554   0.395 
25:01  0.773943 0.776  0.10956  -0.1413    0.3604   0.396 
26:01  0.949529 0.736  0.11067  -0.1398    0.3611   0.390 
29:02  0.877431 0.793  0.11626  -0.1369    0.3694   0.372 
31:01  0.444260 0.238  0.11387  -0.1338    0.3615   0.371 
32:01 -0.005739 0.054  0.16001  -0.0873    0.4073   0.210 
68:01  1.910822 0.193  0.13482  -0.1146    0.3842   0.294 
Logistic regression (additive model) with 60 individuals:
  glm(case ˜ h, family = binomial, data = data)
      [-] [h] %.[-] %.[h] chisq.st chisq.p fisher.p    h.est     h.2.5%
24:02 109  11  46.8  81.8   3.6030  0.058    0.053    1.7918     0.1585
-----                                                                  
01:01  95  25  50.5  48.0   0.0000  1.000    1.000   -0.1207    -1.0843
02:01  77  43  48.1  53.5   0.1450  0.703    0.704    0.2137    -0.5289
02:06 119   1  50.4   0.0   0.0000  1.000    1.000  -15.6000 -2868.1268
03:01 111   9  49.5  55.6   0.0000  1.000    1.000    0.2624    -1.1624
11:01 115   5  50.4  40.0   0.0000  1.000    1.000   -0.4418    -2.3074
23:01 117   3  50.4  33.3   0.0000  1.000    1.000   -0.4323    -2.3435
24:03 119   1  50.4   0.0   0.0000  1.000    1.000  -15.6000 -2868.1268
25:01 115   5  51.3  20.0   0.8348  0.361    0.364   -1.4955    -3.7498
26:01 117   3  51.3   0.0   1.3675  0.242    0.244  -16.6714 -2731.9621
29:02 116   4  50.9  25.0   0.2586  0.611    0.619   -1.1701    -3.4931
31:01 117   3  49.6  66.7   0.0000  1.000    1.000    0.7282    -1.7277
32:01 116   4  48.3 100.0   2.3276  0.127    0.119   17.7092 -3859.2763
68:01 117   3  51.3   0.0   1.3675  0.242    0.244  -16.6714 -2731.9621
        h.97.5% h.pval
24:02    3.4251 0.032*
-----                 
01:01    0.8430 0.806 
02:01    0.9563 0.573 
02:06 2836.9268 0.991 
03:01    1.6872 0.718 
11:01    1.4237 0.643 
23:01    1.4789 0.658 
24:03 2836.9268 0.991 
25:01    0.7588 0.194 
26:01 2698.6192 0.990 
29:02    1.1530 0.324 
31:01    3.1842 0.561 
32:01 3894.6947 0.993 
68:01 2698.6192 0.990 
Logistic regression (recessive model) with 60 individuals:
  glm(case ˜ h, family = binomial, data = data)
      [-/-,-/h] [h/h] %.[-/-,-/h] %.[h/h] chisq.st chisq.p fisher.p   h.est
01:01        59     1        50.8       0    0.000  1.000    1.000  -15.600
02:01        52     8        46.2      75    1.298  0.255    0.254    1.253
02:06        60     0        50.0       .        .       .        .       .
03:01        60     0        50.0       .        .       .        .       .
11:01        60     0        50.0       .        .       .        .       .
23:01        59     1        50.8       0    0.000  1.000    1.000  -15.600
24:02        60     0        50.0       .        .       .        .       .
24:03        60     0        50.0       .        .       .        .       .
25:01        60     0        50.0       .        .       .        .       .
26:01        60     0        50.0       .        .       .        .       .
29:02        60     0        50.0       .        .       .        .       .
31:01        60     0        50.0       .        .       .        .       .
32:01        60     0        50.0       .        .       .        .       .
68:01        60     0        50.0       .        .       .        .       .
          h.2.5%  h.97.5% h.pval
01:01 -2868.1268 2836.927 0.991 
02:01    -0.4379    2.943 0.146 
02:06          .        .      .
03:01          .        .      .
11:01          .        .      .
23:01 -2868.1268 2836.927 0.991 
24:02          .        .      .
24:03          .        .      .
25:01          .        .      .
26:01          .        .      .
29:02          .        .      .
31:01          .        .      .
32:01          .        .      .
68:01          .        .      .
Logistic regression (genotype model) with 60 individuals:
  glm(case ˜ h, family = binomial, data = data)
      [-/-] [-/h] [h/h] %.[-/-] %.[-/h] %.[h/h] chisq.st chisq.p fisher.p
24:02    49    11     0    42.9    81.8       .   4.0074  0.045*   0.042*
-----                                                                    
01:01    36    23     1    50.0    52.2       0   1.0435  0.593    1.000 
02:01    25    27     8    52.0    40.7      75   2.9659  0.227    0.271 
02:06    59     1     0    50.8     0.0       .   0.0000  1.000    1.000 
03:01    51     9     0    49.0    55.6       .   0.0000  1.000    1.000 
11:01    55     5     0    50.9    40.0       .   0.0000  1.000    1.000 
23:01    58     1     1    50.0   100.0       0   2.0000  0.368    1.000 
24:03    59     1     0    50.8     0.0       .   0.0000  1.000    1.000 
25:01    55     5     0    52.7    20.0       .   0.8727  0.350    0.353 
26:01    57     3     0    52.6     0.0       .   1.4035  0.236    0.237 
29:02    56     4     0    51.8    25.0       .   0.2679  0.605    0.612 
31:01    57     3     0    49.1    66.7       .   0.0000  1.000    1.000 
32:01    56     4     0    46.4   100.0       .   2.4107  0.121    0.112 
68:01    57     3     0    52.6     0.0       .   1.4035  0.236    0.237 
         h1.est    h1.2.5%  h1.97.5% h1.pval  h2.est    h2.2.5% h2.97.5%
24:02   1.79176     0.1585    3.4251  0.032*       .          .        .
-----                                                                   
01:01   0.08701    -0.9600    1.1340  0.871  -15.566 -2868.0929 2836.961
02:01  -0.45474    -1.5524    0.6430  0.417    1.019    -0.7637    2.801
02:06 -15.59997 -2868.1268 2836.9268  0.991        .          .        .
03:01   0.26236    -1.1624    1.6872  0.718        .          .        .
11:01  -0.44183    -2.3074    1.4237  0.643        .          .        .
23:01  16.56607 -4686.4552 4719.5873  0.994  -16.566 -4719.5873 4686.455
24:03 -15.59997 -2868.1268 2836.9268  0.991        .          .        .
25:01  -1.49549    -3.7498    0.7588  0.194        .          .        .
26:01 -16.67143 -2731.9621 2698.6192  0.990        .          .        .
29:02  -1.17007    -3.4931    1.1530  0.324        .          .        .
31:01   0.72824    -1.7277    3.1842  0.561        .          .        .
32:01  17.70917 -3859.2763 3894.6947  0.993        .          .        .
68:01 -16.67143 -2731.9621 2698.6192  0.990        .          .        .
      h2.pval
24:02       .
-----        
01:01  0.991 
02:01  0.263 
02:06       .
03:01       .
11:01       .
23:01  0.994 
24:03       .
25:01       .
26:01       .
29:02       .
31:01       .
32:01       .
68:01       .
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:58
     1, SNP: 155, Loss: 186.198, OOB Acc: 68.18%, # of Haplo: 13
     2, SNP: 111, Loss: 138.902, OOB Acc: 72.73%, # of Haplo: 13
     3, SNP: 159, Loss: 134.624, OOB Acc: 81.82%, # of Haplo: 15
     4, SNP: 75, Loss: 120.53, OOB Acc: 86.36%, # of Haplo: 16
     5, SNP: 129, Loss: 79.3436, OOB Acc: 90.91%, # of Haplo: 20
     6, SNP: 135, Loss: 56.5856, OOB Acc: 90.91%, # of Haplo: 20
     7, SNP: 162, Loss: 40.1347, OOB Acc: 90.91%, # of Haplo: 20
     8, SNP: 195, Loss: 29.7565, OOB Acc: 90.91%, # of Haplo: 24
     9, SNP: 182, Loss: 23.1586, OOB Acc: 90.91%, # of Haplo: 24
    10, SNP: 24, Loss: 16.7007, OOB Acc: 90.91%, # of Haplo: 27
    11, SNP: 154, Loss: 15.6368, OOB Acc: 90.91%, # of Haplo: 28
    12, SNP: 17, Loss: 15.4059, OOB Acc: 90.91%, # of Haplo: 39
    13, SNP: 199, Loss: 14.4894, OOB Acc: 90.91%, # of Haplo: 40
    14, SNP: 73, Loss: 8.03154, OOB Acc: 90.91%, # of Haplo: 42
    15, SNP: 151, Loss: 5.75619, OOB Acc: 90.91%, # of Haplo: 42
[1] 2019-04-13 03:03:58, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
     1, SNP: 87, Loss: 163.788, OOB Acc: 50.00%, # of Haplo: 15
     2, SNP: 179, Loss: 159.036, OOB Acc: 57.69%, # of Haplo: 16
     3, SNP: 162, Loss: 127.744, OOB Acc: 61.54%, # of Haplo: 17
     4, SNP: 113, Loss: 120.348, OOB Acc: 76.92%, # of Haplo: 17
     5, SNP: 149, Loss: 99.7027, OOB Acc: 80.77%, # of Haplo: 19
     6, SNP: 183, Loss: 63.9201, OOB Acc: 80.77%, # of Haplo: 19
     7, SNP: 82, Loss: 46.9454, OOB Acc: 80.77%, # of Haplo: 19
     8, SNP: 73, Loss: 36.2707, OOB Acc: 84.62%, # of Haplo: 21
     9, SNP: 47, Loss: 34.9876, OOB Acc: 84.62%, # of Haplo: 22
    10, SNP: 140, Loss: 27.9494, OOB Acc: 84.62%, # of Haplo: 22
    11, SNP: 189, Loss: 23.9141, OOB Acc: 84.62%, # of Haplo: 23
    12, SNP: 249, Loss: 18.884, OOB Acc: 84.62%, # of Haplo: 27
    13, SNP: 151, Loss: 15.8511, OOB Acc: 84.62%, # of Haplo: 27
    14, SNP: 21, Loss: 11.1982, OOB Acc: 88.46%, # of Haplo: 30
    15, SNP: 215, Loss: 10.7762, OOB Acc: 88.46%, # of Haplo: 32
    16, SNP: 136, Loss: 10.5472, OOB Acc: 88.46%, # of Haplo: 38
    17, SNP: 75, Loss: 10.4276, OOB Acc: 88.46%, # of Haplo: 49
    18, SNP: 28, Loss: 10.3043, OOB Acc: 88.46%, # of Haplo: 55
    19, SNP: 266, Loss: 10.3043, OOB Acc: 92.31%, # of Haplo: 48
    20, SNP: 199, Loss: 9.5327, OOB Acc: 92.31%, # of Haplo: 48
[2] 2019-04-13 03:03:58, OOB Acc: 92.31%, # of SNPs: 20, # of Haplo: 48
     1, SNP: 167, Loss: 182.217, OOB Acc: 55.88%, # of Haplo: 14
     2, SNP: 126, Loss: 120.313, OOB Acc: 64.71%, # of Haplo: 16
     3, SNP: 135, Loss: 94.3559, OOB Acc: 73.53%, # of Haplo: 19
     4, SNP: 173, Loss: 73.5379, OOB Acc: 79.41%, # of Haplo: 19
     5, SNP: 196, Loss: 47.4931, OOB Acc: 85.29%, # of Haplo: 20
     6, SNP: 124, Loss: 28.5275, OOB Acc: 88.24%, # of Haplo: 20
     7, SNP: 112, Loss: 27.3051, OOB Acc: 88.24%, # of Haplo: 22
     8, SNP: 150, Loss: 26.8966, OOB Acc: 88.24%, # of Haplo: 22
     9, SNP: 106, Loss: 20.7953, OOB Acc: 88.24%, # of Haplo: 21
    10, SNP: 116, Loss: 20.4753, OOB Acc: 88.24%, # of Haplo: 21
    11, SNP: 16, Loss: 20.2453, OOB Acc: 88.24%, # of Haplo: 24
    12, SNP: 6, Loss: 20.1953, OOB Acc: 88.24%, # of Haplo: 28
    13, SNP: 181, Loss: 19.9842, OOB Acc: 88.24%, # of Haplo: 28
    14, SNP: 73, Loss: 11.7972, OOB Acc: 91.18%, # of Haplo: 33
    15, SNP: 96, Loss: 11.7743, OOB Acc: 91.18%, # of Haplo: 37
    16, SNP: 177, Loss: 11.7597, OOB Acc: 91.18%, # of Haplo: 45
    17, SNP: 151, Loss: 11.3816, OOB Acc: 91.18%, # of Haplo: 45
    18, SNP: 199, Loss: 10.1586, OOB Acc: 91.18%, # of Haplo: 45
[3] 2019-04-13 03:03:58, OOB Acc: 91.18%, # of SNPs: 18, # of Haplo: 45
     1, SNP: 191, Loss: 189.049, OOB Acc: 63.64%, # of Haplo: 15
     2, SNP: 100, Loss: 160.313, OOB Acc: 72.73%, # of Haplo: 18
     3, SNP: 158, Loss: 135.403, OOB Acc: 77.27%, # of Haplo: 19
     4, SNP: 86, Loss: 112.795, OOB Acc: 86.36%, # of Haplo: 22
     5, SNP: 134, Loss: 75.8819, OOB Acc: 86.36%, # of Haplo: 24
     6, SNP: 85, Loss: 54.3124, OOB Acc: 90.91%, # of Haplo: 29
     7, SNP: 128, Loss: 36.9547, OOB Acc: 90.91%, # of Haplo: 29
     8, SNP: 77, Loss: 30.0981, OOB Acc: 90.91%, # of Haplo: 32
     9, SNP: 54, Loss: 24.7805, OOB Acc: 90.91%, # of Haplo: 32
    10, SNP: 21, Loss: 20.945, OOB Acc: 90.91%, # of Haplo: 36
    11, SNP: 199, Loss: 20.2197, OOB Acc: 90.91%, # of Haplo: 36
    12, SNP: 266, Loss: 19.48, OOB Acc: 90.91%, # of Haplo: 38
    13, SNP: 52, Loss: 19.1914, OOB Acc: 90.91%, # of Haplo: 38
    14, SNP: 215, Loss: 18.9375, OOB Acc: 90.91%, # of Haplo: 38
    15, SNP: 73, Loss: 14.4966, OOB Acc: 90.91%, # of Haplo: 38
    16, SNP: 151, Loss: 13.3459, OOB Acc: 90.91%, # of Haplo: 39
[4] 2019-04-13 03:03:58, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 39
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0001216782 0.0001372891 0.0002777870 0.0023012412 0.0072873069 0.0234246874 
        Max.         Mean           SD 
0.2886695022 0.0296925310 0.0666389121 
Accuracy with training data: 98.5%
Out-of-bag accuracy: 91.3%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 54
    avg. # of SNPs in an individual classifier: 17.25
        (sd: 2.22, min: 15, max: 20, median: 17.00)
    avg. # of haplotypes in an individual classifier: 43.50
        (sd: 3.87, min: 39, max: 48, median: 43.50)
    avg. out-of-bag accuracy: 91.33%
        (sd: 0.67%, min: 90.91%, max: 92.31%, median: 91.04%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0001216782 0.0001372891 0.0002777870 0.0023012412 0.0072873069 0.0234246874 
        Max.         Mean           SD 
0.2886695022 0.0296925310 0.0666389121 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:58)	0%
Predicting (2019-04-13 03:03:58)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.8%)   2 (7.7%)  3 (11.5%) 20 (76.9%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0007878 0.0047578 0.0193435 0.0128352 0.2886695 
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:58)	0%
Predicting (2019-04-13 03:03:58)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 14
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (3.8%)   2 (7.7%)  3 (11.5%) 20 (76.9%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0007878 0.0047578 0.0193435 0.0128352 0.2886695 
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
SNP genotypes: 
    90 samples X 3932 SNPs
    SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
    min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
    min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
 A/G  C/T  G/T  A/C  C/G  A/T 
1567 1510  348  332  111   64 
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 5316 SNPs from chromosome 6.
SNP genotypes: 
    90 samples X 5316 SNPs
    SNPs range from 25651262bp to 33426848bp on hg19
Missing rate per SNP:
    min: 0, max: 0.1, mean: 0.0882054, median: 0.1, sd: 0.030674
Missing rate per sample:
    min: 0, max: 0.863619, mean: 0.0882054, median: 0.00131678, sd: 0.259735
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.201867, median: 0.179012, sd: 0.155475
Allelic information:
 A/G  C/T  G/T  A/C  C/G  A/T 
2102 2046  480  471  134   83 
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77, # of samples: 60
# of unique HLA alleles: 12
[-] 2019-04-13 03:03:59
[1] 2019-04-13 03:03:59, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20
[2] 2019-04-13 03:03:59, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 
        Max.         Mean           SD 
4.728168e-01 4.409445e-02 1.068815e-01 
Accuracy with training data: 95.0%
Out-of-bag accuracy: 94.5%
Gene: DQB1
Training dataset: 60 samples X 77 SNPs
    # of HLA alleles: 12
    # of individual classifiers: 2
    total # of SNPs used: 20
    avg. # of SNPs in an individual classifier: 14.00
        (sd: 1.41, min: 13, max: 15, median: 14.00)
    avg. # of haplotypes in an individual classifier: 20.50
        (sd: 0.71, min: 20, max: 21, median: 20.50)
    avg. out-of-bag accuracy: 94.45%
        (sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 
        Max.         Mean           SD 
4.728168e-01 4.409445e-02 1.068815e-01 
Genome assembly: hg19
The HIBAG model:
	There are 77 SNP predictors in total.
	There are 2 individual classifiers.
Summarize the missing fractions of SNP predictors per classifier:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0       0       0       0       0       0 
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 60
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 100
# of unique HLA alleles: 0
# of unique HLA genotypes: 0
Gene: C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 200
# of unique HLA alleles: 17
# of unique HLA genotypes: 35
Exclude 9 monomorphic SNPs
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:59
[1] 2019-04-13 03:03:59, OOB Acc: 86.96%, # of SNPs: 12, # of Haplo: 32
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
4.166789e-14 4.261245e-14 5.111347e-14 2.589270e-03 1.608934e-02 5.868848e-02 
        Max.         Mean           SD 
6.267394e-01 6.664806e-02 1.405453e-01 
Accuracy with training data: 94.2%
Out-of-bag accuracy: 87.0%
Exclude 9 monomorphic SNPs
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:59
[1] 2019-04-13 03:03:59, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 40
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.894066e-24 9.219565e-20 9.218854e-19 2.189685e-03 7.704546e-03 2.406258e-02 
        Max.         Mean           SD 
2.755151e-01 2.949891e-02 6.162169e-02 
Accuracy with training data: 95.0%
Out-of-bag accuracy: 87.5%
Gene: A
Training dataset: 60 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 2
    total # of SNPs used: 24
    avg. # of SNPs in an individual classifier: 13.50
        (sd: 2.12, min: 12, max: 15, median: 13.50)
    avg. # of haplotypes in an individual classifier: 36.00
        (sd: 5.66, min: 32, max: 40, median: 36.00)
    avg. out-of-bag accuracy: 87.23%
        (sd: 0.38%, min: 86.96%, max: 87.50%, median: 87.23%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
9.233104e-13 5.204084e-10 5.195775e-09 2.309655e-03 1.448839e-02 3.746431e-02 
        Max.         Mean           SD 
4.511273e-01 4.807348e-02 1.006148e-01 
Genome assembly: hg19
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:03:59
     1, SNP: 155, Loss: 186.198, OOB Acc: 68.18%, # of Haplo: 13
     2, SNP: 111, Loss: 138.902, OOB Acc: 72.73%, # of Haplo: 13
     3, SNP: 159, Loss: 134.624, OOB Acc: 81.82%, # of Haplo: 15
     4, SNP: 75, Loss: 120.53, OOB Acc: 86.36%, # of Haplo: 16
     5, SNP: 129, Loss: 79.3436, OOB Acc: 90.91%, # of Haplo: 20
     6, SNP: 135, Loss: 56.5856, OOB Acc: 90.91%, # of Haplo: 20
     7, SNP: 162, Loss: 40.1347, OOB Acc: 90.91%, # of Haplo: 20
     8, SNP: 195, Loss: 29.7565, OOB Acc: 90.91%, # of Haplo: 24
     9, SNP: 182, Loss: 23.1586, OOB Acc: 90.91%, # of Haplo: 24
    10, SNP: 24, Loss: 16.7007, OOB Acc: 90.91%, # of Haplo: 27
    11, SNP: 154, Loss: 15.6368, OOB Acc: 90.91%, # of Haplo: 28
    12, SNP: 17, Loss: 15.4059, OOB Acc: 90.91%, # of Haplo: 39
    13, SNP: 199, Loss: 14.4894, OOB Acc: 90.91%, # of Haplo: 40
    14, SNP: 73, Loss: 8.03154, OOB Acc: 90.91%, # of Haplo: 42
    15, SNP: 151, Loss: 5.75619, OOB Acc: 90.91%, # of Haplo: 42
[1] 2019-04-13 03:03:59, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 42
     1, SNP: 87, Loss: 163.788, OOB Acc: 50.00%, # of Haplo: 15
     2, SNP: 179, Loss: 159.036, OOB Acc: 57.69%, # of Haplo: 16
     3, SNP: 162, Loss: 127.744, OOB Acc: 61.54%, # of Haplo: 17
     4, SNP: 113, Loss: 120.348, OOB Acc: 76.92%, # of Haplo: 17
     5, SNP: 149, Loss: 99.7027, OOB Acc: 80.77%, # of Haplo: 19
     6, SNP: 183, Loss: 63.9201, OOB Acc: 80.77%, # of Haplo: 19
     7, SNP: 82, Loss: 46.9454, OOB Acc: 80.77%, # of Haplo: 19
     8, SNP: 73, Loss: 36.2707, OOB Acc: 84.62%, # of Haplo: 21
     9, SNP: 47, Loss: 34.9876, OOB Acc: 84.62%, # of Haplo: 22
    10, SNP: 140, Loss: 27.9494, OOB Acc: 84.62%, # of Haplo: 22
    11, SNP: 189, Loss: 23.9141, OOB Acc: 84.62%, # of Haplo: 23
    12, SNP: 249, Loss: 18.884, OOB Acc: 84.62%, # of Haplo: 27
    13, SNP: 151, Loss: 15.8511, OOB Acc: 84.62%, # of Haplo: 27
    14, SNP: 21, Loss: 11.1982, OOB Acc: 88.46%, # of Haplo: 30
    15, SNP: 215, Loss: 10.7762, OOB Acc: 88.46%, # of Haplo: 32
    16, SNP: 136, Loss: 10.5472, OOB Acc: 88.46%, # of Haplo: 38
    17, SNP: 75, Loss: 10.4276, OOB Acc: 88.46%, # of Haplo: 49
    18, SNP: 28, Loss: 10.3043, OOB Acc: 88.46%, # of Haplo: 55
    19, SNP: 266, Loss: 10.3043, OOB Acc: 92.31%, # of Haplo: 48
    20, SNP: 199, Loss: 9.5327, OOB Acc: 92.31%, # of Haplo: 48
[2] 2019-04-13 03:03:59, OOB Acc: 92.31%, # of SNPs: 20, # of Haplo: 48
     1, SNP: 167, Loss: 182.217, OOB Acc: 55.88%, # of Haplo: 14
     2, SNP: 126, Loss: 120.313, OOB Acc: 64.71%, # of Haplo: 16
     3, SNP: 135, Loss: 94.3559, OOB Acc: 73.53%, # of Haplo: 19
     4, SNP: 173, Loss: 73.5379, OOB Acc: 79.41%, # of Haplo: 19
     5, SNP: 196, Loss: 47.4931, OOB Acc: 85.29%, # of Haplo: 20
     6, SNP: 124, Loss: 28.5275, OOB Acc: 88.24%, # of Haplo: 20
     7, SNP: 112, Loss: 27.3051, OOB Acc: 88.24%, # of Haplo: 22
     8, SNP: 150, Loss: 26.8966, OOB Acc: 88.24%, # of Haplo: 22
     9, SNP: 106, Loss: 20.7953, OOB Acc: 88.24%, # of Haplo: 21
    10, SNP: 116, Loss: 20.4753, OOB Acc: 88.24%, # of Haplo: 21
    11, SNP: 16, Loss: 20.2453, OOB Acc: 88.24%, # of Haplo: 24
    12, SNP: 6, Loss: 20.1953, OOB Acc: 88.24%, # of Haplo: 28
    13, SNP: 181, Loss: 19.9842, OOB Acc: 88.24%, # of Haplo: 28
    14, SNP: 73, Loss: 11.7972, OOB Acc: 91.18%, # of Haplo: 33
    15, SNP: 96, Loss: 11.7743, OOB Acc: 91.18%, # of Haplo: 37
    16, SNP: 177, Loss: 11.7597, OOB Acc: 91.18%, # of Haplo: 45
    17, SNP: 151, Loss: 11.3816, OOB Acc: 91.18%, # of Haplo: 45
    18, SNP: 199, Loss: 10.1586, OOB Acc: 91.18%, # of Haplo: 45
[3] 2019-04-13 03:03:59, OOB Acc: 91.18%, # of SNPs: 18, # of Haplo: 45
     1, SNP: 191, Loss: 189.049, OOB Acc: 63.64%, # of Haplo: 15
     2, SNP: 100, Loss: 160.313, OOB Acc: 72.73%, # of Haplo: 18
     3, SNP: 158, Loss: 135.403, OOB Acc: 77.27%, # of Haplo: 19
     4, SNP: 86, Loss: 112.795, OOB Acc: 86.36%, # of Haplo: 22
     5, SNP: 134, Loss: 75.8819, OOB Acc: 86.36%, # of Haplo: 24
     6, SNP: 85, Loss: 54.3124, OOB Acc: 90.91%, # of Haplo: 29
     7, SNP: 128, Loss: 36.9547, OOB Acc: 90.91%, # of Haplo: 29
     8, SNP: 77, Loss: 30.0981, OOB Acc: 90.91%, # of Haplo: 32
     9, SNP: 54, Loss: 24.7805, OOB Acc: 90.91%, # of Haplo: 32
    10, SNP: 21, Loss: 20.945, OOB Acc: 90.91%, # of Haplo: 36
    11, SNP: 199, Loss: 20.2197, OOB Acc: 90.91%, # of Haplo: 36
    12, SNP: 266, Loss: 19.48, OOB Acc: 90.91%, # of Haplo: 38
    13, SNP: 52, Loss: 19.1914, OOB Acc: 90.91%, # of Haplo: 38
    14, SNP: 215, Loss: 18.9375, OOB Acc: 90.91%, # of Haplo: 38
    15, SNP: 73, Loss: 14.4966, OOB Acc: 90.91%, # of Haplo: 38
    16, SNP: 151, Loss: 13.3459, OOB Acc: 90.91%, # of Haplo: 39
[4] 2019-04-13 03:03:59, OOB Acc: 90.91%, # of SNPs: 16, # of Haplo: 39
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0001216782 0.0001372891 0.0002777870 0.0023012412 0.0072873069 0.0234246874 
        Max.         Mean           SD 
0.2886695022 0.0296925310 0.0666389121 
Accuracy with training data: 98.5%
Out-of-bag accuracy: 91.3%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 54
    avg. # of SNPs in an individual classifier: 17.25
        (sd: 2.22, min: 15, max: 20, median: 17.00)
    avg. # of haplotypes in an individual classifier: 43.50
        (sd: 3.87, min: 39, max: 48, median: 43.50)
    avg. out-of-bag accuracy: 91.33%
        (sd: 0.67%, min: 90.91%, max: 92.31%, median: 91.04%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0001216782 0.0001372891 0.0002777870 0.0023012412 0.0072873069 0.0234246874 
        Max.         Mean           SD 
0.2886695022 0.0296925310 0.0666389121 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:03:59)	0%
Predicting (2019-04-13 03:03:59)	100%
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 01:01, 02:02
Allelic ambiguity: 09:01
Allelic ambiguity: 09:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 05:01, 06:01
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num   *   -   A   D   E   F   G   H   I   K   L   M   N   Q   R   S   T   V   W   Y 
  1 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  9 120   .  81   .   .   .   .   .   .   .   .   .   .   .   .   .  15   7   .   .  17 
 44 120   .  25   .   .   .   .   .   .   .   .   .   .   .   .  95   .   .   .   .   . 
 56 120   . 117   .   .   .   .   .   .   .   .   .   .   .   .   3   .   .   .   .   . 
 62 120   .  46   .   .  15   .  44   .   .   .   4   .   .   .  11   .   .   .   .   . 
 63 120   . 105   .   .   .   .   .   .   .   .   .   .  11   4   .   .   .   .   .   . 
 65 120   . 105   .   .   .   .  15   .   .   .   .   .   .   .   .   .   .   .   .   . 
 66 120   .  61   .   .   .   .   .   .   .  59   .   .   .   .   .   .   .   .   .   . 
 67 120   .  25   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  95   .   . 
 70 120   .  99   .   .   .   .   .   .   .   .   .   .   .  21   .   .   .   .   .   . 
 73 120   . 117   .   .   .   .   .   .   3   .   .   .   .   .   .   .   .   .   .   . 
 74 120   .  76   .   .   .   .   .  44   .   .   .   .   .   .   .   .   .   .   .   . 
 76 120   .  32   .   .  24   .   .   .   .   .   .   .   .   .   .   .   .  64   .   . 
 77 120   .  47   .  64   .   .   .   .   .   .   .   .   .   .   .   9   .   .   .   . 
 79 120   .  96   .   .   .   .   .   .   .   .   .   .   .   .  24   .   .   .   .   . 
 80 120   .  96   .   .   .   .   .   .  24   .   .   .   .   .   .   .   .   .   .   . 
 81 120   .  96  24   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 82 120   .  96   .   .   .   .   .   .   .   .  24   .   .   .   .   .   .   .   .   . 
 83 120   .  96   .   .   .   .   .   .   .   .   .   .   .   .  24   .   .   .   .   . 
 90 120   .  38  82   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 95 120   .  61   .   .   .   .   .   .   .   .  15   .   .   .   .   .   .  44   .   . 
 97 120   .  39   .   .   .   .   .   .   .   .   .  29   .   .  52   .   .   .   .   . 
 99 120   . 105   .   .   .  15   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
105 120   .  42   .   .   .   .   .   .   .   .   .   .   .   .   .  78   .   .   .   . 
107 120   .  76   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  44   . 
109 120   . 116   .   .   .   .   .   .   .   .   4   .   .   .   .   .   .   .   .   . 
114 120   .  46   .   .   .   .   .  59   .   .   .   .   .  15   .   .   .   .   .   . 
116 120   .  61   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  59 
127 120   .  58   .   .   .   .   .   .   .  62   .   .   .   .   .   .   .   .   .   . 
142 120   .  73   .   .   .   .   .   .   .   .   .   .   .   .   .   .  47   .   .   . 
144 120   .  98   .   .   .   .   .   .   .   .   .   .   .  22   .   .   .   .   .   . 
145 120   .  73   .   .   .   .   .  47   .   .   .   .   .   .   .   .   .   .   .   . 
149 120   . 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   8   .   .   . 
150 120   .  25  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
151 120   . 106   .   .   .   .   .   .   .   .   .   .   .   .  14   .   .   .   .   . 
152 120   .  30   .   .  17   .   .   .   .   .   .   .   .   .   .   .   .  73   .   . 
156 120   .  25   .   .   .   .   .   .   .   .  67   .   .  17   .   .   .   .  11   . 
158 120   .  25  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
161 120   . 111   .   9   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
163 120   .  38   .   .   .   .   .   .   .   .   .   .   .   .   .   .  82   .   .   . 
166 120   .  39   .   .  81   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
167 120   .  39   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  81   . 
183 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
Allelic ambiguity: 01:01, 03:01, 26:01, 02:01, 23:01, 02:06, 11:01, 25:01, 31:01, 24:02, 29:02, 68:01, 24:03, 32:01
Pos Num   *   -   A   D   E   F   G   H   I   K   L   M   N   Q   R   S   T   V   W   Y 
-23 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-22 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-21 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-20 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-19 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-18 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-17 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-16 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-15 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-14 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-13 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-12 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-11 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-10 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -9 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -8 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -7 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -6 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -5 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -4 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -3 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -2 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -1 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  . 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  1 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  9 120   .  81   .   .   .   .   .   .   .   .   .   .   .   .   .  15   7   .   .  17 
 44 120   .  25   .   .   .   .   .   .   .   .   .   .   .   .  95   .   .   .   .   . 
 56 120   . 117   .   .   .   .   .   .   .   .   .   .   .   .   3   .   .   .   .   . 
 62 120   .  46   .   .  15   .  44   .   .   .   4   .   .   .  11   .   .   .   .   . 
 63 120   . 105   .   .   .   .   .   .   .   .   .   .  11   4   .   .   .   .   .   . 
 65 120   . 105   .   .   .   .  15   .   .   .   .   .   .   .   .   .   .   .   .   . 
 66 120   .  61   .   .   .   .   .   .   .  59   .   .   .   .   .   .   .   .   .   . 
 67 120   .  25   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  95   .   . 
 70 120   .  99   .   .   .   .   .   .   .   .   .   .   .  21   .   .   .   .   .   . 
 73 120   . 117   .   .   .   .   .   .   3   .   .   .   .   .   .   .   .   .   .   . 
 74 120   .  76   .   .   .   .   .  44   .   .   .   .   .   .   .   .   .   .   .   . 
 76 120   .  32   .   .  24   .   .   .   .   .   .   .   .   .   .   .   .  64   .   . 
 77 120   .  47   .  64   .   .   .   .   .   .   .   .   .   .   .   9   .   .   .   . 
 79 120   .  96   .   .   .   .   .   .   .   .   .   .   .   .  24   .   .   .   .   . 
 80 120   .  96   .   .   .   .   .   .  24   .   .   .   .   .   .   .   .   .   .   . 
 81 120   .  96  24   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 82 120   .  96   .   .   .   .   .   .   .   .  24   .   .   .   .   .   .   .   .   . 
 83 120   .  96   .   .   .   .   .   .   .   .   .   .   .   .  24   .   .   .   .   . 
 90 120   .  38  82   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 95 120   .  61   .   .   .   .   .   .   .   .  15   .   .   .   .   .   .  44   .   . 
 97 120   .  39   .   .   .   .   .   .   .   .   .  29   .   .  52   .   .   .   .   . 
 99 120   . 105   .   .   .  15   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
105 120   .  42   .   .   .   .   .   .   .   .   .   .   .   .   .  78   .   .   .   . 
107 120   .  76   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  44   . 
109 120   . 116   .   .   .   .   .   .   .   .   4   .   .   .   .   .   .   .   .   . 
114 120   .  46   .   .   .   .   .  59   .   .   .   .   .  15   .   .   .   .   .   . 
116 120   .  61   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  59 
127 120   .  58   .   .   .   .   .   .   .  62   .   .   .   .   .   .   .   .   .   . 
142 120   .  73   .   .   .   .   .   .   .   .   .   .   .   .   .   .  47   .   .   . 
144 120   .  98   .   .   .   .   .   .   .   .   .   .   .  22   .   .   .   .   .   . 
145 120   .  73   .   .   .   .   .  47   .   .   .   .   .   .   .   .   .   .   .   . 
149 120   . 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   8   .   .   . 
150 120   .  25  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
151 120   . 106   .   .   .   .   .   .   .   .   .   .   .   .  14   .   .   .   .   . 
152 120   .  30   .   .  17   .   .   .   .   .   .   .   .   .   .   .   .  73   .   . 
156 120   .  25   .   .   .   .   .   .   .   .  67   .   .  17   .   .   .   .  11   . 
158 120   .  25  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
161 120   . 111   .   9   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
163 120   .  38   .   .   .   .   .   .   .   .   .   .   .   .   .   .  82   .   .   . 
166 120   .  39   .   .  81   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
167 120   .  39   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .  81   . 
183 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
184 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
185 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
186 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
187 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
188 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
189 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
190 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
191 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
192 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
193 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
194 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
195 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
196 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
197 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
198 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
199 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
200 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
201 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
202 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
203 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
204 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
205 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
206 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
207 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
208 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
209 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
210 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
211 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
212 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
213 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
214 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
215 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
216 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
217 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
218 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
219 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
220 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
221 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
222 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
223 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
224 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
225 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
226 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
227 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
228 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
229 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
230 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
231 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
232 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
233 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
234 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
235 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
236 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
237 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
238 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
239 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
240 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
241 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
242 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
243 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
244 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
245 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
246 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
247 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
248 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
249 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
250 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
251 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
252 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
253 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
254 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
255 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
256 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
257 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
258 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
259 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
260 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
261 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
262 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
263 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
264 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
265 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
266 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
267 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
268 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
269 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
270 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
271 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
272 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
273 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
274 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
275 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
276 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
277 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
278 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
279 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
280 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
281 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
282 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
283 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
284 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
285 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
286 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
287 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
288 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
289 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
290 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
291 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
292 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
293 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
294 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
295 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
296 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
297 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
298 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
299 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
300 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
301 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
302 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
303 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
304 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
305 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
306 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
307 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
308 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
309 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
310 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
311 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
312 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
313 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
314 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
315 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
316 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
317 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
318 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
319 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
320 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
321 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
322 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
323 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
324 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
325 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
326 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
327 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
328 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
329 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
330 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
331 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
332 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
333 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
334 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
335 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
336 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
337 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
338 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
339 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
340 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
341 120 120   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num   *   -   A   D   E   F   G   I   K   L   M   N   P   Q   R   S   T   Y 
  5 120 112   .   .   .   .   .   .   .   .   .   .   8   .   .   .   .   .   . 
  6 120  20  92   8   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  7 112  20  92   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  8 112  20  92   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  9 112   3  76   .   .   .  33   .   .   .   .   .   .   .   .   .   .   .   . 
 10 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 11 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 12 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 13 112   3  93  16   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 14 112   3  14   .   .   .   .   .   .   .   .  95   .   .   .   .   .   .   . 
 15 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 16 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 17 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 18 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 19 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 20 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 26 112   .  20   .   .   .   .   .   .   .  76   .   .   .   .   .   .   .  16 
 28 112   . 100   .   .   .   .   .   .   .   .   .   .   .   .   .  12   .   . 
 30 112   .  24   .   .   .   .   .   .   .   .   .   .   .   .   .  12   .  76 
 37 112   . 100   .   .   .   .   .  12   .   .   .   .   .   .   .   .   .   . 
 38 112   .  29  83   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 45 112   .  96   .   .  16   .   .   .   .   .   .   .   .   .   .   .   .   . 
 46 112   . 100   .   .  12   .   .   .   .   .   .   .   .   .   .   .   .   . 
 47 112   . 100   .   .   .  12   .   .   .   .   .   .   .   .   .   .   .   . 
 52 112   . 100   .   .   .   .   .   .   .  12   .   .   .   .   .   .   .   . 
 53 112   .  54   .   .   .   .   .   .   .  58   .   .   .   .   .   .   .   . 
 55 112   .  57   .   .   .   .   .   .   .  12   .   .  43   .   .   .   .   . 
 56 112   . 109   .   .   .   .   .   .   .   3   .   .   .   .   .   .   .   . 
 57 112   .  14  33  64   .   .   .   .   .   .   .   .   .   .   .   1   .   . 
 66 112   .  97   .  15   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 67 112   .  97   .   .   .   .   .  15   .   .   .   .   .   .   .   .   .   . 
 70 112   3  50   .   .   3   .   .   .   .   .   .   .   .   .  56   .   .   . 
 71 112   3  14   .   3   .   .   .   .  12   .   .   .   .   .   .   .  80   . 
 72 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 73 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 74 112   3  17  12   .  80   .   .   .   .   .   .   .   .   .   .   .   .   . 
 75 112   3  29   .   .   .   .   .   .   .  80   .   .   .   .   .   .   .   . 
 76 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 77 112   3  26   .   .   .   .   .   .   .   .   .   .   .   .   .   .  83   . 
 78 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 79 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 80 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 81 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 82 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 83 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 84 112   3  51   .   .   .   .   .   .   .   .   .   .   .  58   .   .   .   . 
 85 112   3  51   .   .   .   .   .   .   .  58   .   .   .   .   .   .   .   . 
 86 112   3  50   .   .  58   .   1   .   .   .   .   .   .   .   .   .   .   . 
 87 112   3  15   .   .   .  36   .   .   .  58   .   .   .   .   .   .   .   . 
 88 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 89 112   3  51   .   .   .   .   .   .   .   .   .   .   .   .   .   .  58   . 
 90 112   3  51   .   .   .   .   .   .   .   .   .   .   .   .   .   .  58   . 
 91 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 92 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 93 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 94 112  17  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 95 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
Allelic ambiguity: 03:02, 06:02, 05:01, 02:01, 05:03, 03:03, 03:01, 04:02, 06:03, 06:04, 05:02
Pos Num   *   -   A   D   E   F   G   I   K   L   M   N   P   Q   R   S   T   Y 
-31 120 112   .   .   .   .   .   .   .   .   .   .   8   .   .   .   .   .   . 
-30 120 112   .   8   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-29 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-28 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-27 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-26 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-25 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-24 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-23 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-22 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-21 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-20 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-19 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-18 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-17 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-16 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-15 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-14 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-13 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-12 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-11 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
-10 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -9 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -8 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -7 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -6 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -5 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -4 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -3 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -2 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 -1 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  . 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  1 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  2 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  3 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  4 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  5 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  6 112  20  92   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  7 112  20  92   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  8 112  20  92   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
  9 112   3  76   .   .   .  33   .   .   .   .   .   .   .   .   .   .   .   . 
 10 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 11 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 12 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 13 112   3  93  16   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 14 112   3  14   .   .   .   .   .   .   .   .  95   .   .   .   .   .   .   . 
 15 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 16 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 17 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 18 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 19 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 20 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 26 112   .  20   .   .   .   .   .   .   .  76   .   .   .   .   .   .   .  16 
 28 112   . 100   .   .   .   .   .   .   .   .   .   .   .   .   .  12   .   . 
 30 112   .  24   .   .   .   .   .   .   .   .   .   .   .   .   .  12   .  76 
 37 112   . 100   .   .   .   .   .  12   .   .   .   .   .   .   .   .   .   . 
 38 112   .  29  83   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 45 112   .  96   .   .  16   .   .   .   .   .   .   .   .   .   .   .   .   . 
 46 112   . 100   .   .  12   .   .   .   .   .   .   .   .   .   .   .   .   . 
 47 112   . 100   .   .   .  12   .   .   .   .   .   .   .   .   .   .   .   . 
 52 112   . 100   .   .   .   .   .   .   .  12   .   .   .   .   .   .   .   . 
 53 112   .  54   .   .   .   .   .   .   .  58   .   .   .   .   .   .   .   . 
 55 112   .  57   .   .   .   .   .   .   .  12   .   .  43   .   .   .   .   . 
 56 112   . 109   .   .   .   .   .   .   .   3   .   .   .   .   .   .   .   . 
 57 112   .  14  33  64   .   .   .   .   .   .   .   .   .   .   .   1   .   . 
 66 112   .  97   .  15   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 67 112   .  97   .   .   .   .   .  15   .   .   .   .   .   .   .   .   .   . 
 70 112   3  50   .   .   3   .   .   .   .   .   .   .   .   .  56   .   .   . 
 71 112   3  14   .   3   .   .   .   .  12   .   .   .   .   .   .   .  80   . 
 72 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 73 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 74 112   3  17  12   .  80   .   .   .   .   .   .   .   .   .   .   .   .   . 
 75 112   3  29   .   .   .   .   .   .   .  80   .   .   .   .   .   .   .   . 
 76 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 77 112   3  26   .   .   .   .   .   .   .   .   .   .   .   .   .   .  83   . 
 78 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 79 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 80 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 81 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 82 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 83 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 84 112   3  51   .   .   .   .   .   .   .   .   .   .   .  58   .   .   .   . 
 85 112   3  51   .   .   .   .   .   .   .  58   .   .   .   .   .   .   .   . 
 86 112   3  50   .   .  58   .   1   .   .   .   .   .   .   .   .   .   .   . 
 87 112   3  15   .   .   .  36   .   .   .  58   .   .   .   .   .   .   .   . 
 88 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 89 112   3  51   .   .   .   .   .   .   .   .   .   .   .   .   .   .  58   . 
 90 112   3  51   .   .   .   .   .   .   .   .   .   .   .   .   .   .  58   . 
 91 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 92 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 93 112   3 109   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 94 112  17  95   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 95 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 96 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 97 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 98 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
 99 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
100 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
101 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
102 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
103 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
104 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
105 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
106 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
107 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
108 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
109 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
110 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
111 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
112 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
113 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
114 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
115 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
116 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
117 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
118 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
119 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
120 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
121 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
122 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
123 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
124 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
125 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
126 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
127 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
128 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
129 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
130 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
131 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
132 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
133 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
134 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
135 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
136 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
137 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
138 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
139 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
140 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
141 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
142 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
143 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
144 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
145 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
146 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
147 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
148 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
149 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
150 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
151 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
152 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
153 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
154 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
155 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
156 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
157 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
158 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
159 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
160 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
161 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
162 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
163 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
164 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
165 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
166 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
167 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
168 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
169 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
170 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
171 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
172 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
173 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
174 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
175 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
176 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
177 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
178 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
179 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
180 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
181 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
182 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
183 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
184 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
185 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
186 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
187 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
188 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
189 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
190 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
191 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
192 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
193 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
194 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
195 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
196 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
197 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
198 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
199 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
200 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
201 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
202 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
203 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
204 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
205 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
206 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
207 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
208 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
209 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
210 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
211 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
212 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
213 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
214 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
215 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
216 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
217 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
218 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
219 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
220 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
221 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
222 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
223 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
224 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
225 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
226 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
227 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
228 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
229 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
230 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
231 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
232 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
233 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
234 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
235 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
236 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
237 112 112   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   .   . 
SNP genotypes: 
    60 samples X 275 SNPs
    SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
    min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
    min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C 
125  97  32  21 
Exclude 9 monomorphic SNPs
Build a HIBAG model with 10 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:02
[1] 2019-04-13 03:04:02, OOB Acc: 86.96%, # of SNPs: 12, # of Haplo: 32
[2] 2019-04-13 03:04:02, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 40
[3] 2019-04-13 03:04:02, OOB Acc: 97.92%, # of SNPs: 14, # of Haplo: 21
[4] 2019-04-13 03:04:02, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 25
[5] 2019-04-13 03:04:02, OOB Acc: 78.95%, # of SNPs: 14, # of Haplo: 21
[6] 2019-04-13 03:04:02, OOB Acc: 93.75%, # of SNPs: 16, # of Haplo: 22
[7] 2019-04-13 03:04:03, OOB Acc: 93.75%, # of SNPs: 24, # of Haplo: 81
[8] 2019-04-13 03:04:03, OOB Acc: 92.86%, # of SNPs: 20, # of Haplo: 45
[9] 2019-04-13 03:04:03, OOB Acc: 94.74%, # of SNPs: 16, # of Haplo: 45
[10] 2019-04-13 03:04:03, OOB Acc: 97.37%, # of SNPs: 15, # of Haplo: 40
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0001493079 0.0001617044 0.0002732730 0.0039571951 0.0150902995 0.0320853535 
        Max.         Mean           SD 
0.3770420992 0.0416903408 0.0825371577 
Accuracy with training data: 98.3%
Out-of-bag accuracy: 91.9%
Gene: A
Training dataset: 60 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 10
    total # of SNPs used: 95
    avg. # of SNPs in an individual classifier: 16.00
        (sd: 3.50, min: 12, max: 24, median: 15.00)
    avg. # of haplotypes in an individual classifier: 37.20
        (sd: 18.22, min: 21, max: 81, median: 36.00)
    avg. out-of-bag accuracy: 91.92%
        (sd: 5.83%, min: 78.95%, max: 97.92%, median: 93.75%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0001493079 0.0001617044 0.0002732730 0.0039571951 0.0150902995 0.0320853535 
        Max.         Mean           SD 
0.3770420992 0.0416903408 0.0825371577 
Genome assembly: hg19
SNP genotypes: 
    60 samples X 275 SNPs
    SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
    min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
    min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C 
125  97  32  21 
SNP genotypes: 
    60 samples X 275 SNPs
    SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
    min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
    min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C 
125  97  32  21 
Loading required namespace: gdsfmt
Loading required namespace: SNPRelate
Import 2348 SNPs from chromosome 6.
3 SNPs with invalid alleles have been removed.
SNP genotypes: 
    165 samples X 2345 SNPs
    SNPs range from 24652946bp to 33524089bp on hg18
Missing rate per SNP:
    min: 0, max: 0.0484848, mean: 0.001698, median: 0, sd: 0.00504919
Missing rate per sample:
    min: 0, max: 0.0110874, mean: 0.001698, median: 0.000852878, sd: 0.00195105
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.194167, median: 0.172727, sd: 0.149319
Allelic information:
A/G T/C G/A C/T T/G A/C C/A G/T C/G G/C A/T T/A 
556 467 410 400 116 109 103  81  30  29  28  16 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
No allelic strand orders are switched.
SNP genotypes: 
    150 samples X 1214 SNPs
    SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0866667, mean: 0.0844646, median: 0.0866667, sd: 0.0128841
Missing rate per sample:
    min: 0, max: 0.968699, mean: 0.0844646, median: 0.000823723, sd: 0.273119
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.234168, median: 0.218978, sd: 0.137855
Allelic information:
A/G C/T G/T A/C 
505 496 109 104 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
SNP genotypes: 
    60 samples X 1197 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0657059, median: 0.0666667, sd: 0.00757446
Missing rate per sample:
    min: 0, max: 0.978279, mean: 0.0657059, median: 0.000835422, sd: 0.245786
Minor allele frequency:
    min: 0.101695, max: 0.5, mean: 0.278734, median: 0.267857, sd: 0.117338
Allelic information:
A/G C/T A/C G/T 
511 476 105 105 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bed" in the individual-major mode.
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.fam".
Open "C:/Users/biocbuild/bbs-3.8-bioc/R/library/HIBAG/extdata/HapMap_CEU.bim".
Import 3932 SNPs within the xMHC region on chromosome 6.
SNP genotypes: 
    90 samples X 3932 SNPs
    SNPs range from 28694391bp to 33426848bp on hg19
Missing rate per SNP:
    min: 0, max: 0.1, mean: 0.0888861, median: 0.1, sd: 0.0298489
Missing rate per sample:
    min: 0, max: 0.869786, mean: 0.0888861, median: 0.00101729, sd: 0.261554
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.210453, median: 0.191358, sd: 0.155144
Allelic information:
 A/G  C/T  G/T  A/C  C/G  A/T 
1567 1510  348  332  111   64 
No allelic strand orders are switched.
SNP genotypes: 
    60 samples X 1214 SNPs
    SNPs range from 28695148bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0650879, median: 0.0666667, sd: 0.0097381
Missing rate per sample:
    min: 0, max: 0.968699, mean: 0.0650879, median: 0.000823723, sd: 0.243373
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.234476, median: 0.223214, sd: 0.13833
Allelic information:
A/G C/T G/T A/C 
505 496 109 104 
SNP genotypes: 
    60 samples X 275 SNPs
    SNPs range from 29417816bp to 30410205bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0652727, median: 0.0666667, sd: 0.00939558
Missing rate per sample:
    min: 0, max: 0.974545, mean: 0.0652727, median: 0, sd: 0.245066
Minor allele frequency:
    min: 0, max: 0.491071, mean: 0.215181, median: 0.1875, sd: 0.139271
Allelic information:
C/T A/G G/T A/C 
125  97  32  21 
MAF filter (>=0.01), excluding 9 SNP(s)
using the default genome assembly (assembly="hg19")
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:04:05
[1] 2019-04-13 03:04:05, OOB Acc: 92.00%, # of SNPs: 24, # of Haplo: 29
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.222247e-28 1.128571e-24 1.128371e-23 6.944660e-04 8.333349e-03 3.673611e-02 
        Max.         Mean           SD 
9.105734e-02 2.054649e-02 2.598603e-02 
Accuracy with training data: 96.7%
Out-of-bag accuracy: 92.0%
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:04:05
[1] 2019-04-13 03:04:06, OOB Acc: 97.50%, # of SNPs: 18, # of Haplo: 34
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
5.014366e-13 4.671716e-10 4.667203e-09 1.640727e-03 7.504546e-03 2.126745e-02 
        Max.         Mean           SD 
9.784316e-02 1.490504e-02 1.947399e-02 
Accuracy with training data: 97.5%
Out-of-bag accuracy: 97.5%
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:04:06
[1] 2019-04-13 03:04:06, OOB Acc: 88.89%, # of SNPs: 14, # of Haplo: 38
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.222223e-18 6.603163e-16 6.583163e-15 1.944468e-03 1.020834e-02 4.122739e-02 
        Max.         Mean           SD 
1.808372e-01 2.422083e-02 3.699146e-02 
Accuracy with training data: 95.8%
Out-of-bag accuracy: 88.9%
Gene: C
Training dataset: 60 samples X 83 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 3
    total # of SNPs used: 40
    avg. # of SNPs in an individual classifier: 18.67
        (sd: 5.03, min: 14, max: 24, median: 18.00)
    avg. # of haplotypes in an individual classifier: 33.67
        (sd: 4.51, min: 29, max: 38, median: 34.00)
    avg. out-of-bag accuracy: 92.80%
        (sd: 4.36%, min: 88.89%, max: 97.50%, median: 92.00%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
1.708707e-13 1.229313e-05 1.229313e-04 1.860746e-03 9.050936e-03 3.332722e-02 
        Max.         Mean           SD 
1.210500e-01 1.989079e-02 2.507466e-02 
Genome assembly: hg19
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 9
# of SNPs: 77, # of samples: 60
# of unique HLA alleles: 12
[-] 2019-04-13 03:04:06
[1] 2019-04-13 03:04:06, OOB Acc: 98.00%, # of SNPs: 13, # of Haplo: 20
[2] 2019-04-13 03:04:06, OOB Acc: 90.91%, # of SNPs: 15, # of Haplo: 21
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 
        Max.         Mean           SD 
4.728168e-01 4.409445e-02 1.068815e-01 
Accuracy with training data: 95.0%
Out-of-bag accuracy: 94.5%
Gene: DQB1
Training dataset: 60 samples X 77 SNPs
    # of HLA alleles: 12
    # of individual classifiers: 2
    total # of SNPs used: 20
    avg. # of SNPs in an individual classifier: 14.00
        (sd: 1.41, min: 13, max: 15, median: 14.00)
    avg. # of haplotypes in an individual classifier: 20.50
        (sd: 0.71, min: 20, max: 21, median: 20.50)
    avg. out-of-bag accuracy: 94.45%
        (sd: 5.01%, min: 90.91%, max: 98.00%, median: 94.45%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.678603e-08 2.465759e-05 2.458848e-04 5.217343e-03 1.419916e-02 2.994924e-02 
        Max.         Mean           SD 
4.728168e-01 4.409445e-02 1.068815e-01 
Genome assembly: hg19
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:06
[1] 2019-04-13 03:04:06, OOB Acc: 86.96%, # of SNPs: 12, # of Haplo: 32
[2] 2019-04-13 03:04:06, OOB Acc: 87.50%, # of SNPs: 15, # of Haplo: 40
[3] 2019-04-13 03:04:06, OOB Acc: 97.92%, # of SNPs: 14, # of Haplo: 21
[4] 2019-04-13 03:04:06, OOB Acc: 95.45%, # of SNPs: 14, # of Haplo: 25
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777 
        Max.         Mean           SD 
0.3714064677 0.0407831671 0.0808783377 
Accuracy with training data: 99.2%
Out-of-bag accuracy: 92.0%
Gene: A
Training dataset: 60 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 42
    avg. # of SNPs in an individual classifier: 13.75
        (sd: 1.26, min: 12, max: 15, median: 14.00)
    avg. # of haplotypes in an individual classifier: 29.50
        (sd: 8.35, min: 21, max: 40, median: 28.50)
    avg. out-of-bag accuracy: 91.96%
        (sd: 5.56%, min: 86.96%, max: 97.92%, median: 91.48%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777 
        Max.         Mean           SD 
0.3714064677 0.0407831671 0.0808783377 
Genome assembly: hg19
Gene: A
Training dataset: 60 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 42
    avg. # of SNPs in an individual classifier: 13.75
        (sd: 1.26, min: 12, max: 15, median: 14.00)
    avg. # of haplotypes in an individual classifier: 29.50
        (sd: 8.35, min: 21, max: 40, median: 28.50)
    avg. out-of-bag accuracy: 91.96%
        (sd: 5.56%, min: 86.96%, max: 97.92%, median: 91.48%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002130783 0.0002168959 0.0002512539 0.0038875845 0.0168866292 0.0328690777 
        Max.         Mean           SD 
0.3714064677 0.0407831671 0.0808783377 
Genome assembly: hg19
Sat Apr 13 03:04:06 2019, passing the 1/4 classifiers.
Sat Apr 13 03:04:06 2019, passing the 2/4 classifiers.
Sat Apr 13 03:04:06 2019, passing the 3/4 classifiers.
Sat Apr 13 03:04:06 2019, passing the 4/4 classifiers.
Allele	Num.	Freq.	CR	ACC	SEN	SPE	PPV	NPV	Miscall
	Valid.	Valid.	(%)	(%)	(%)	(%)	(%)	(%)	(%)
----
Overall accuracy: 92.0%, Call rate: 100.0%
01:01 25 0.2083 100.0 100.0 100.0 100.0 100.0 100.0 --
02:01 43 0.3583 100.0 96.7 100.0 95.1 92.5 100.0 --
02:06 1 0.0083 25.0 97.7 0.0 100.0 -- 97.7 02:01 (100)
03:01 9 0.0750 100.0 100.0 100.0 100.0 100.0 100.0 --
11:01 5 0.0417 100.0 100.0 100.0 100.0 100.0 100.0 --
23:01 3 0.0250 100.0 98.4 75.0 100.0 100.0 98.4 24:02 (100)
24:02 11 0.0917 100.0 97.3 100.0 97.1 76.2 100.0 --
24:03 1 0.0083 100.0 97.8 0.0 100.0 -- 97.8 24:02 (75)
25:01 5 0.0417 100.0 98.4 100.0 98.3 84.7 100.0 --
26:01 3 0.0250 100.0 98.4 62.5 100.0 100.0 98.4 25:01 (83)
29:02 4 0.0333 100.0 97.8 75.0 100.0 100.0 97.8 02:01 (75)
31:01 3 0.0250 75.0 100.0 100.0 100.0 100.0 100.0 --
32:01 4 0.0333 100.0 100.0 100.0 100.0 100.0 100.0 --
68:01 3 0.0250 100.0 100.0 100.0 100.0 100.0 100.0 --
\title{Imputation Evaluation}

\documentclass[12pt]{article}

\usepackage{fullpage}
\usepackage{longtable}

\begin{document}

\maketitle

\setlength{\LTcapwidth}{6.5in}

% -------- BEGIN TABLE --------
\begin{longtable}{rrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
 & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{10}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
 & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{10}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{10}{l}{\it Overall accuracy: 92.0\%, Call rate: 100.0\%} \\
01:01 & 25 & 0.2083 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
02:01 & 43 & 0.3583 & 100.0 & 96.7 & 100.0 & 95.1 & 92.5 & 100.0 & -- \\
02:06 & 1 & 0.0083 & 25.0 & 97.7 & 0.0 & 100.0 & -- & 97.7 & 02:01 (100) \\
03:01 & 9 & 0.0750 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
11:01 & 5 & 0.0417 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
23:01 & 3 & 0.0250 & 100.0 & 98.4 & 75.0 & 100.0 & 100.0 & 98.4 & 24:02 (100) \\
24:02 & 11 & 0.0917 & 100.0 & 97.3 & 100.0 & 97.1 & 76.2 & 100.0 & -- \\
24:03 & 1 & 0.0083 & 100.0 & 97.8 & 0.0 & 100.0 & -- & 97.8 & 24:02 (75) \\
25:01 & 5 & 0.0417 & 100.0 & 98.4 & 100.0 & 98.3 & 84.7 & 100.0 & -- \\
26:01 & 3 & 0.0250 & 100.0 & 98.4 & 62.5 & 100.0 & 100.0 & 98.4 & 25:01 (83) \\
29:02 & 4 & 0.0333 & 100.0 & 97.8 & 75.0 & 100.0 & 100.0 & 97.8 & 02:01 (75) \\
31:01 & 3 & 0.0250 & 75.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
32:01 & 4 & 0.0333 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 3 & 0.0250 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
\end{longtable}
% -------- END TABLE --------

\end{document}
<!DOCTYPE html>
<html>
<head>
  <title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1"  CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="10">
<i> Overall accuracy: 92.0%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>25</td> <td>0.2083</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>43</td> <td>0.3583</td> <td>100.0</td> <td>96.7</td> <td>100.0</td> <td>95.1</td> <td>92.5</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0083</td> <td>25.0</td> <td>97.7</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.7</td> <td>02:01 (100)</td>
</tr>
<tr>
<td>03:01</td> <td>9</td> <td>0.0750</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>11:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>98.4</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>98.4</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>24:02</td> <td>11</td> <td>0.0917</td> <td>100.0</td> <td>97.3</td> <td>100.0</td> <td>97.1</td> <td>76.2</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0083</td> <td>100.0</td> <td>97.8</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>97.8</td> <td>24:02 (75)</td>
</tr>
<tr>
<td>25:01</td> <td>5</td> <td>0.0417</td> <td>100.0</td> <td>98.4</td> <td>100.0</td> <td>98.3</td> <td>84.7</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>26:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>98.4</td> <td>62.5</td> <td>100.0</td> <td>100.0</td> <td>98.4</td> <td>25:01 (83)</td>
</tr>
<tr>
<td>29:02</td> <td>4</td> <td>0.0333</td> <td>100.0</td> <td>97.8</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>97.8</td> <td>02:01 (75)</td>
</tr>
<tr>
<td>31:01</td> <td>3</td> <td>0.0250</td> <td>75.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>32:01</td> <td>4</td> <td>0.0333</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>3</td> <td>0.0250</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
</table>

</body>
</html>
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 24
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes:
2019-04-13 03:04:07,   1, job  2, # of SNPs: 13, # of haplo: 25, acc: 77.3%
  --  avg out-of-bag acc: 77.27%, sd: NA%, min: 77.27%, max: 77.27%
2019-04-13 03:04:07,   2, job  1, # of SNPs: 15, # of haplo: 29, acc: 68.2%
  --  avg out-of-bag acc: 72.73%, sd: 6.43%, min: 68.18%, max: 77.27%
2019-04-13 03:04:07,   3, job  1, # of SNPs: 11, # of haplo: 20, acc: 75.0%
  --  avg out-of-bag acc: 73.48%, sd: 4.73%, min: 68.18%, max: 77.27%
2019-04-13 03:04:07,   4, job  2, # of SNPs: 16, # of haplo: 48, acc: 92.3%
  --  avg out-of-bag acc: 78.19%, sd: 10.17%, min: 68.18%, max: 92.31%
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0005947299 0.0006009795 0.0006572256 0.0035994277 0.0077211451 0.0217891437 
        Max.         Mean           SD 
0.3112910741 0.0363824443 0.0749359074 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 78.2%
Build a HIBAG model of 4 individual classifiers in parallel with 2 compute nodes:
The model is autosaved in 'tmp_model.RData'.
2019-04-13 03:04:08,   1, job  2, # of SNPs: 15, # of haplo: 28, acc: 79.2%
  --  avg out-of-bag acc: 79.17%, sd: NA%, min: 79.17%, max: 79.17%
2019-04-13 03:04:08,   2, job  1, # of SNPs: 16, # of haplo: 43, acc: 87.5%
  --  avg out-of-bag acc: 83.33%, sd: 5.89%, min: 79.17%, max: 87.50%
2019-04-13 03:04:08,   3, job  1, # of SNPs: 14, # of haplo: 31, acc: 87.5%
Stop "job 1".
  --  avg out-of-bag acc: 84.72%, sd: 4.81%, min: 79.17%, max: 87.50%
2019-04-13 03:04:08,   4, job  1, # of SNPs: 12, # of haplo: 40, acc: 87.5%
Stop "job 1".
  --  avg out-of-bag acc: 85.42%, sd: 4.17%, min: 79.17%, max: 87.50%
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0005406630 0.0005513682 0.0006477149 0.0032481280 0.0069351986 0.0129974119 
        Max.         Mean           SD 
0.3957005129 0.0344092581 0.0907182263 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 85.4%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 48
    avg. # of SNPs in an individual classifier: 14.25
        (sd: 1.71, min: 12, max: 16, median: 14.50)
    avg. # of haplotypes in an individual classifier: 35.50
        (sd: 7.14, min: 28, max: 43, median: 35.50)
    avg. out-of-bag accuracy: 85.42%
        (sd: 4.17%, min: 79.17%, max: 87.50%, median: 87.50%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0005406630 0.0005513682 0.0006477149 0.0032481280 0.0069351986 0.0129974119 
        Max.         Mean           SD 
0.3957005129 0.0344092581 0.0907182263 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:04:08)	0%
Predicting (2019-04-13 03:04:08)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  2 (7.7%)   2 (7.7%)  5 (19.2%) 17 (65.4%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0006556 0.0045330 0.0231821 0.0136119 0.3957005 
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Run in parallel with 2 compute nodes.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  2 (7.7%)   2 (7.7%)  5 (19.2%) 17 (65.4%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0006556 0.0045330 0.0231821 0.0136119 0.3957005 
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:09
     1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
     2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
     3, SNP: 106, Loss: 123.308, OOB Acc: 77.27%, # of Haplo: 16
     4, SNP: 87, Loss: 77.8709, OOB Acc: 81.82%, # of Haplo: 20
     5, SNP: 114, Loss: 43.8425, OOB Acc: 90.91%, # of Haplo: 20
     6, SNP: 7, Loss: 36.5344, OOB Acc: 90.91%, # of Haplo: 23
     7, SNP: 199, Loss: 26.705, OOB Acc: 90.91%, # of Haplo: 24
     8, SNP: 201, Loss: 16.5421, OOB Acc: 90.91%, # of Haplo: 26
     9, SNP: 102, Loss: 15.7295, OOB Acc: 90.91%, # of Haplo: 33
    10, SNP: 144, Loss: 15.078, OOB Acc: 90.91%, # of Haplo: 33
    11, SNP: 52, Loss: 14.8361, OOB Acc: 90.91%, # of Haplo: 42
    12, SNP: 108, Loss: 14.7598, OOB Acc: 90.91%, # of Haplo: 42
    13, SNP: 73, Loss: 11.7396, OOB Acc: 90.91%, # of Haplo: 43
    14, SNP: 151, Loss: 11.3074, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:04:09, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 45
     1, SNP: 45, Loss: 180.067, OOB Acc: 50.00%, # of Haplo: 15
     2, SNP: 75, Loss: 135.995, OOB Acc: 60.71%, # of Haplo: 18
     3, SNP: 176, Loss: 100.071, OOB Acc: 64.29%, # of Haplo: 24
     4, SNP: 181, Loss: 72.877, OOB Acc: 67.86%, # of Haplo: 34
     5, SNP: 154, Loss: 60.9088, OOB Acc: 71.43%, # of Haplo: 35
     6, SNP: 180, Loss: 56.0063, OOB Acc: 71.43%, # of Haplo: 39
     7, SNP: 164, Loss: 51.6779, OOB Acc: 71.43%, # of Haplo: 48
     8, SNP: 227, Loss: 47.3381, OOB Acc: 71.43%, # of Haplo: 50
     9, SNP: 197, Loss: 40.1255, OOB Acc: 71.43%, # of Haplo: 50
    10, SNP: 165, Loss: 38.8164, OOB Acc: 71.43%, # of Haplo: 49
    11, SNP: 151, Loss: 34.5811, OOB Acc: 71.43%, # of Haplo: 49
    12, SNP: 222, Loss: 34.3146, OOB Acc: 71.43%, # of Haplo: 53
    13, SNP: 111, Loss: 34.3145, OOB Acc: 75.00%, # of Haplo: 61
    14, SNP: 73, Loss: 27.4168, OOB Acc: 75.00%, # of Haplo: 61
    15, SNP: 132, Loss: 27.3856, OOB Acc: 75.00%, # of Haplo: 61
    16, SNP: 199, Loss: 25.7831, OOB Acc: 75.00%, # of Haplo: 61
[2] 2019-04-13 03:04:09, OOB Acc: 75.00%, # of SNPs: 16, # of Haplo: 61
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.632299e-08 2.701820e-06 2.678129e-05 1.297587e-03 1.145581e-02 1.694929e-02 
        Max.         Mean           SD 
3.194254e-01 2.861672e-02 7.433563e-02 
Accuracy with training data: 97.1%
Out-of-bag accuracy: 83.0%
Exclude 9 monomorphic SNPs
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:09
     1, SNP: 119, Loss: 233.718, OOB Acc: 50.00%, # of Haplo: 13
     2, SNP: 232, Loss: 181.18, OOB Acc: 52.78%, # of Haplo: 16
     3, SNP: 71, Loss: 139.471, OOB Acc: 58.33%, # of Haplo: 18
     4, SNP: 166, Loss: 116.525, OOB Acc: 61.11%, # of Haplo: 20
     5, SNP: 116, Loss: 87.4554, OOB Acc: 63.89%, # of Haplo: 24
     6, SNP: 126, Loss: 42.9667, OOB Acc: 66.67%, # of Haplo: 30
     7, SNP: 33, Loss: 32.6021, OOB Acc: 66.67%, # of Haplo: 41
     8, SNP: 102, Loss: 30.9058, OOB Acc: 69.44%, # of Haplo: 41
     9, SNP: 169, Loss: 30.4075, OOB Acc: 72.22%, # of Haplo: 41
    10, SNP: 148, Loss: 29.993, OOB Acc: 72.22%, # of Haplo: 41
    11, SNP: 87, Loss: 29.5166, OOB Acc: 72.22%, # of Haplo: 41
    12, SNP: 70, Loss: 24.11, OOB Acc: 72.22%, # of Haplo: 41
    13, SNP: 151, Loss: 23.5686, OOB Acc: 72.22%, # of Haplo: 42
[1] 2019-04-13 03:04:09, OOB Acc: 72.22%, # of SNPs: 13, # of Haplo: 42
     1, SNP: 102, Loss: 142.289, OOB Acc: 38.46%, # of Haplo: 15
     2, SNP: 75, Loss: 110.14, OOB Acc: 46.15%, # of Haplo: 19
     3, SNP: 214, Loss: 91.7293, OOB Acc: 53.85%, # of Haplo: 19
     4, SNP: 121, Loss: 63.0626, OOB Acc: 57.69%, # of Haplo: 19
     5, SNP: 194, Loss: 34.2678, OOB Acc: 57.69%, # of Haplo: 23
     6, SNP: 203, Loss: 21.9942, OOB Acc: 57.69%, # of Haplo: 28
     7, SNP: 130, Loss: 18.122, OOB Acc: 61.54%, # of Haplo: 36
     8, SNP: 14, Loss: 12.615, OOB Acc: 61.54%, # of Haplo: 38
     9, SNP: 17, Loss: 13.5634, OOB Acc: 65.38%, # of Haplo: 41
    10, SNP: 122, Loss: 12.8134, OOB Acc: 65.38%, # of Haplo: 41
    11, SNP: 199, Loss: 12.3319, OOB Acc: 65.38%, # of Haplo: 41
    12, SNP: 134, Loss: 11.1247, OOB Acc: 65.38%, # of Haplo: 41
    13, SNP: 128, Loss: 10.7586, OOB Acc: 65.38%, # of Haplo: 41
    14, SNP: 223, Loss: 10.3119, OOB Acc: 65.38%, # of Haplo: 42
    15, SNP: 37, Loss: 10.0841, OOB Acc: 65.38%, # of Haplo: 44
    16, SNP: 185, Loss: 9.84428, OOB Acc: 65.38%, # of Haplo: 44
    17, SNP: 151, Loss: 9.21691, OOB Acc: 65.38%, # of Haplo: 44
    18, SNP: 73, Loss: 7.59606, OOB Acc: 65.38%, # of Haplo: 46
    19, SNP: 127, Loss: 7.59606, OOB Acc: 69.23%, # of Haplo: 46
    20, SNP: 110, Loss: 7.59606, OOB Acc: 73.08%, # of Haplo: 46
    21, SNP: 54, Loss: 9.02042, OOB Acc: 76.92%, # of Haplo: 52
    22, SNP: 184, Loss: 7.59607, OOB Acc: 76.92%, # of Haplo: 52
[2] 2019-04-13 03:04:09, OOB Acc: 76.92%, # of SNPs: 22, # of Haplo: 52
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.703386e-13 1.713083e-09 1.712840e-08 2.197928e-03 5.839109e-03 1.241815e-02 
        Max.         Mean           SD 
5.871033e-01 4.193081e-02 1.385420e-01 
Accuracy with training data: 91.2%
Out-of-bag accuracy: 74.6%
HIBAG model: 2 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting by voting from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:04:09)	0%
Predicting (2019-04-13 03:04:09)	100%
HIBAG model: 2 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting by voting from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:04:09)	0%
Predicting (2019-04-13 03:04:09)	100%
Exclude 1 monomorphic SNP
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 13
# of SNPs: 158, # of samples: 60
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:10
     1, SNP: 141, Loss: 378.06, OOB Acc: 52.08%, # of Haplo: 14
     2, SNP: 74, Loss: 262.497, OOB Acc: 58.33%, # of Haplo: 15
     3, SNP: 78, Loss: 162.497, OOB Acc: 68.75%, # of Haplo: 19
     4, SNP: 118, Loss: 70.0426, OOB Acc: 72.92%, # of Haplo: 23
     5, SNP: 82, Loss: 45.8279, OOB Acc: 83.33%, # of Haplo: 23
     6, SNP: 95, Loss: 35.4414, OOB Acc: 89.58%, # of Haplo: 27
     7, SNP: 89, Loss: 32.6134, OOB Acc: 89.58%, # of Haplo: 35
     8, SNP: 83, Loss: 31.7921, OOB Acc: 89.58%, # of Haplo: 51
     9, SNP: 151, Loss: 31.0653, OOB Acc: 89.58%, # of Haplo: 55
    10, SNP: 94, Loss: 31.0246, OOB Acc: 89.58%, # of Haplo: 55
    11, SNP: 111, Loss: 18.9027, OOB Acc: 89.58%, # of Haplo: 56
    12, SNP: 139, Loss: 18.4248, OOB Acc: 89.58%, # of Haplo: 59
    13, SNP: 93, Loss: 17.0195, OOB Acc: 91.67%, # of Haplo: 58
    14, SNP: 15, Loss: 14.1692, OOB Acc: 91.67%, # of Haplo: 60
[1] 2019-04-13 03:04:10, OOB Acc: 91.67%, # of SNPs: 14, # of Haplo: 60
     1, SNP: 94, Loss: 403.365, OOB Acc: 63.16%, # of Haplo: 15
     2, SNP: 82, Loss: 294.053, OOB Acc: 71.05%, # of Haplo: 18
     3, SNP: 57, Loss: 226.142, OOB Acc: 76.32%, # of Haplo: 23
     4, SNP: 155, Loss: 197.199, OOB Acc: 84.21%, # of Haplo: 29
     5, SNP: 44, Loss: 132.804, OOB Acc: 86.84%, # of Haplo: 40
     6, SNP: 30, Loss: 122.507, OOB Acc: 92.11%, # of Haplo: 40
     7, SNP: 109, Loss: 72.0179, OOB Acc: 92.11%, # of Haplo: 41
     8, SNP: 72, Loss: 59.3281, OOB Acc: 92.11%, # of Haplo: 41
     9, SNP: 36, Loss: 54.939, OOB Acc: 94.74%, # of Haplo: 43
    10, SNP: 127, Loss: 48.1392, OOB Acc: 94.74%, # of Haplo: 43
    11, SNP: 53, Loss: 44.7676, OOB Acc: 94.74%, # of Haplo: 43
    12, SNP: 148, Loss: 43.047, OOB Acc: 94.74%, # of Haplo: 44
    13, SNP: 152, Loss: 40.2104, OOB Acc: 94.74%, # of Haplo: 45
    14, SNP: 125, Loss: 39.8862, OOB Acc: 94.74%, # of Haplo: 45
    15, SNP: 78, Loss: 39.5652, OOB Acc: 94.74%, # of Haplo: 45
    16, SNP: 3, Loss: 39.0621, OOB Acc: 94.74%, # of Haplo: 47
    17, SNP: 141, Loss: 37.6822, OOB Acc: 94.74%, # of Haplo: 47
    18, SNP: 1, Loss: 36.5022, OOB Acc: 94.74%, # of Haplo: 50
[2] 2019-04-13 03:04:10, OOB Acc: 94.74%, # of SNPs: 18, # of Haplo: 50
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 
        Max.         Mean           SD 
5.948644e-01 6.324378e-02 1.404915e-01 
Accuracy with training data: 96.7%
Out-of-bag accuracy: 93.2%
Gene: A
Training dataset: 60 samples X 158 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 2
    total # of SNPs used: 28
    avg. # of SNPs in an individual classifier: 16.00
        (sd: 2.83, min: 14, max: 18, median: 16.00)
    avg. # of haplotypes in an individual classifier: 55.00
        (sd: 7.07, min: 50, max: 60, median: 55.00)
    avg. out-of-bag accuracy: 93.20%
        (sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 
        Max.         Mean           SD 
5.948644e-01 6.324378e-02 1.404915e-01 
Genome assembly: hg19
Remove 130 unused SNPs.
Gene: A
Training dataset: 60 samples X 28 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 2
    total # of SNPs used: 28
    avg. # of SNPs in an individual classifier: 16.00
        (sd: 2.83, min: 14, max: 18, median: 16.00)
    avg. # of haplotypes in an individual classifier: 55.00
        (sd: 7.07, min: 50, max: 60, median: 55.00)
    avg. out-of-bag accuracy: 93.20%
        (sd: 2.17%, min: 91.67%, max: 94.74%, median: 93.20%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
8.333397e-09 5.342641e-08 4.592635e-07 2.372425e-03 1.515625e-02 5.190505e-02 
        Max.         Mean           SD 
5.948644e-01 6.324378e-02 1.404915e-01 
Genome assembly: hg19
Platform: Illumina 1M Duo 
Information: Training set -- HapMap Phase II 
HIBAG model: 2 individual classifiers, 158 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 60
Predicting (2019-04-13 03:04:10)	0%
Predicting (2019-04-13 03:04:10)	100%
HIBAG model: 2 individual classifiers, 28 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 60
Predicting (2019-04-13 03:04:10)	0%
Predicting (2019-04-13 03:04:10)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:10
     1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
     2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
     3, SNP: 106, Loss: 123.308, OOB Acc: 77.27%, # of Haplo: 16
     4, SNP: 87, Loss: 77.8709, OOB Acc: 81.82%, # of Haplo: 20
     5, SNP: 114, Loss: 43.8425, OOB Acc: 90.91%, # of Haplo: 20
     6, SNP: 7, Loss: 36.5344, OOB Acc: 90.91%, # of Haplo: 23
     7, SNP: 199, Loss: 26.705, OOB Acc: 90.91%, # of Haplo: 24
     8, SNP: 201, Loss: 16.5421, OOB Acc: 90.91%, # of Haplo: 26
     9, SNP: 102, Loss: 15.7295, OOB Acc: 90.91%, # of Haplo: 33
    10, SNP: 144, Loss: 15.078, OOB Acc: 90.91%, # of Haplo: 33
    11, SNP: 52, Loss: 14.8361, OOB Acc: 90.91%, # of Haplo: 42
    12, SNP: 108, Loss: 14.7598, OOB Acc: 90.91%, # of Haplo: 42
    13, SNP: 73, Loss: 11.7396, OOB Acc: 90.91%, # of Haplo: 43
    14, SNP: 151, Loss: 11.3074, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:04:10, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 45
     1, SNP: 45, Loss: 180.067, OOB Acc: 50.00%, # of Haplo: 15
     2, SNP: 75, Loss: 135.995, OOB Acc: 60.71%, # of Haplo: 18
     3, SNP: 176, Loss: 100.071, OOB Acc: 64.29%, # of Haplo: 24
     4, SNP: 181, Loss: 72.877, OOB Acc: 67.86%, # of Haplo: 34
     5, SNP: 154, Loss: 60.9088, OOB Acc: 71.43%, # of Haplo: 35
     6, SNP: 180, Loss: 56.0063, OOB Acc: 71.43%, # of Haplo: 39
     7, SNP: 164, Loss: 51.6779, OOB Acc: 71.43%, # of Haplo: 48
     8, SNP: 227, Loss: 47.3381, OOB Acc: 71.43%, # of Haplo: 50
     9, SNP: 197, Loss: 40.1255, OOB Acc: 71.43%, # of Haplo: 50
    10, SNP: 165, Loss: 38.8164, OOB Acc: 71.43%, # of Haplo: 49
    11, SNP: 151, Loss: 34.5811, OOB Acc: 71.43%, # of Haplo: 49
    12, SNP: 222, Loss: 34.3146, OOB Acc: 71.43%, # of Haplo: 53
    13, SNP: 111, Loss: 34.3145, OOB Acc: 75.00%, # of Haplo: 61
    14, SNP: 73, Loss: 27.4168, OOB Acc: 75.00%, # of Haplo: 61
    15, SNP: 132, Loss: 27.3856, OOB Acc: 75.00%, # of Haplo: 61
    16, SNP: 199, Loss: 25.7831, OOB Acc: 75.00%, # of Haplo: 61
[2] 2019-04-13 03:04:10, OOB Acc: 75.00%, # of SNPs: 16, # of Haplo: 61
     1, SNP: 119, Loss: 233.718, OOB Acc: 50.00%, # of Haplo: 13
     2, SNP: 232, Loss: 181.18, OOB Acc: 52.78%, # of Haplo: 16
     3, SNP: 71, Loss: 139.471, OOB Acc: 58.33%, # of Haplo: 18
     4, SNP: 166, Loss: 116.525, OOB Acc: 61.11%, # of Haplo: 20
     5, SNP: 116, Loss: 87.4554, OOB Acc: 63.89%, # of Haplo: 24
     6, SNP: 126, Loss: 42.9667, OOB Acc: 66.67%, # of Haplo: 30
     7, SNP: 33, Loss: 32.6021, OOB Acc: 66.67%, # of Haplo: 41
     8, SNP: 102, Loss: 30.9058, OOB Acc: 69.44%, # of Haplo: 41
     9, SNP: 169, Loss: 30.4075, OOB Acc: 72.22%, # of Haplo: 41
    10, SNP: 148, Loss: 29.993, OOB Acc: 72.22%, # of Haplo: 41
    11, SNP: 87, Loss: 29.5166, OOB Acc: 72.22%, # of Haplo: 41
    12, SNP: 70, Loss: 24.11, OOB Acc: 72.22%, # of Haplo: 41
    13, SNP: 151, Loss: 23.5686, OOB Acc: 72.22%, # of Haplo: 42
[3] 2019-04-13 03:04:10, OOB Acc: 72.22%, # of SNPs: 13, # of Haplo: 42
     1, SNP: 102, Loss: 142.289, OOB Acc: 38.46%, # of Haplo: 15
     2, SNP: 75, Loss: 110.14, OOB Acc: 46.15%, # of Haplo: 19
     3, SNP: 214, Loss: 91.7293, OOB Acc: 53.85%, # of Haplo: 19
     4, SNP: 121, Loss: 63.0626, OOB Acc: 57.69%, # of Haplo: 19
     5, SNP: 194, Loss: 34.2678, OOB Acc: 57.69%, # of Haplo: 23
     6, SNP: 203, Loss: 21.9942, OOB Acc: 57.69%, # of Haplo: 28
     7, SNP: 130, Loss: 18.122, OOB Acc: 61.54%, # of Haplo: 36
     8, SNP: 14, Loss: 12.615, OOB Acc: 61.54%, # of Haplo: 38
     9, SNP: 17, Loss: 13.5634, OOB Acc: 65.38%, # of Haplo: 41
    10, SNP: 122, Loss: 12.8134, OOB Acc: 65.38%, # of Haplo: 41
    11, SNP: 199, Loss: 12.3319, OOB Acc: 65.38%, # of Haplo: 41
    12, SNP: 134, Loss: 11.1247, OOB Acc: 65.38%, # of Haplo: 41
    13, SNP: 128, Loss: 10.7586, OOB Acc: 65.38%, # of Haplo: 41
    14, SNP: 223, Loss: 10.3119, OOB Acc: 65.38%, # of Haplo: 42
    15, SNP: 37, Loss: 10.0841, OOB Acc: 65.38%, # of Haplo: 44
    16, SNP: 185, Loss: 9.84428, OOB Acc: 65.38%, # of Haplo: 44
    17, SNP: 151, Loss: 9.21691, OOB Acc: 65.38%, # of Haplo: 44
    18, SNP: 73, Loss: 7.59606, OOB Acc: 65.38%, # of Haplo: 46
    19, SNP: 127, Loss: 7.59606, OOB Acc: 69.23%, # of Haplo: 46
    20, SNP: 110, Loss: 7.59606, OOB Acc: 73.08%, # of Haplo: 46
    21, SNP: 54, Loss: 9.02042, OOB Acc: 76.92%, # of Haplo: 52
    22, SNP: 184, Loss: 7.59607, OOB Acc: 76.92%, # of Haplo: 52
[4] 2019-04-13 03:04:10, OOB Acc: 76.92%, # of SNPs: 22, # of Haplo: 52
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002162630 0.0002210881 0.0002645141 0.0039247319 0.0075458074 0.0145272758 
        Max.         Mean           SD 
0.4532643335 0.0352737673 0.1063114572 
Accuracy with training data: 94.1%
Out-of-bag accuracy: 78.8%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 54
    avg. # of SNPs in an individual classifier: 16.25
        (sd: 4.03, min: 13, max: 22, median: 15.00)
    avg. # of haplotypes in an individual classifier: 50.00
        (sd: 8.45, min: 42, max: 61, median: 48.50)
    avg. out-of-bag accuracy: 78.76%
        (sd: 8.32%, min: 72.22%, max: 90.91%, median: 75.96%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002162630 0.0002210881 0.0002645141 0.0039247319 0.0075458074 0.0145272758 
        Max.         Mean           SD 
0.4532643335 0.0352737673 0.1063114572 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:04:10)	0%
Predicting (2019-04-13 03:04:10)	100%
Allele	Num.	Freq.	Num.	Freq.	CR	ACC	SEN	SPE	PPV	NPV	Miscall
	Train	Train	Valid.	Valid.	(%)	(%)	(%)	(%)	(%)	(%)	(%)
----
Overall accuracy: 94.2%, Call rate: 100.0%
01:01 13 0.1912 12 0.2308 100.0 100.0 100.0 100.0 100.0 100.0 --
02:01 23 0.3382 20 0.3846 100.0 100.0 100.0 100.0 100.0 100.0 --
02:06 1 0.0147 0 0 -- -- -- -- -- -- --
03:01 4 0.0588 5 0.0962 100.0 100.0 100.0 100.0 100.0 100.0 --
11:01 3 0.0441 2 0.0385 100.0 98.1 100.0 98.0 66.7 100.0 --
23:01 2 0.0294 1 0.0192 100.0 98.1 0.0 100.0 -- 98.1 24:02 (100)
24:02 6 0.0882 5 0.0962 100.0 98.1 100.0 97.9 83.3 100.0 --
24:03 1 0.0147 0 0 -- -- -- -- -- -- --
25:01 3 0.0441 2 0.0385 100.0 98.1 50.0 100.0 100.0 98.0 26:01 (100)
26:01 2 0.0294 1 0.0192 100.0 98.1 100.0 98.0 50.0 100.0 --
29:02 3 0.0441 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
31:01 2 0.0294 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
32:01 3 0.0441 1 0.0192 100.0 100.0 100.0 100.0 100.0 100.0 --
68:01 2 0.0294 1 0.0192 100.0 98.1 0.0 100.0 -- 98.1 11:01 (100)
\title{Imputation Evaluation}

\documentclass[12pt]{article}

\usepackage{fullpage}
\usepackage{longtable}

\begin{document}

\maketitle

\setlength{\LTcapwidth}{6.5in}

% -------- BEGIN TABLE --------
\begin{longtable}{rrrrr | rrrrrrl}
\caption{The sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and call rate (CR).}
\label{tab:accuracy} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
 & Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endfirsthead
\multicolumn{12}{c}{{\normalsize \tablename\ \thetable{} -- Continued from previous page}} \\
Allele & Num. & Freq. & Num. & Freq. & CR & ACC & SEN & SPE & PPV & NPV & Miscall \\
 & Train & Train & Valid. & Valid. & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) & (\%) \\
\hline\hline
\endhead
\hline
\multicolumn{12}{r}{Continued on next page ...} \\
\hline
\endfoot
\hline\hline
\endlastfoot
\multicolumn{12}{l}{\it Overall accuracy: 94.2\%, Call rate: 100.0\%} \\
01:01 & 13 & 0.1912 & 12 & 0.2308 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
02:01 & 23 & 0.3382 & 20 & 0.3846 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
02:06 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
03:01 & 4 & 0.0588 & 5 & 0.0962 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
11:01 & 3 & 0.0441 & 2 & 0.0385 & 100.0 & 98.1 & 100.0 & 98.0 & 66.7 & 100.0 & -- \\
23:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 0.0 & 100.0 & -- & 98.1 & 24:02 (100) \\
24:02 & 6 & 0.0882 & 5 & 0.0962 & 100.0 & 98.1 & 100.0 & 97.9 & 83.3 & 100.0 & -- \\
24:03 & 1 & 0.0147 & 0 & 0 & -- & -- & -- & -- & -- & -- & -- \\
25:01 & 3 & 0.0441 & 2 & 0.0385 & 100.0 & 98.1 & 50.0 & 100.0 & 100.0 & 98.0 & 26:01 (100) \\
26:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 100.0 & 98.0 & 50.0 & 100.0 & -- \\
29:02 & 3 & 0.0441 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
31:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
32:01 & 3 & 0.0441 & 1 & 0.0192 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & -- \\
68:01 & 2 & 0.0294 & 1 & 0.0192 & 100.0 & 98.1 & 0.0 & 100.0 & -- & 98.1 & 11:01 (100) \\
\end{longtable}
% -------- END TABLE --------

\end{document}
<!DOCTYPE html>
<html>
<head>
  <title>Imputation Evaluation</title>
</head>
<body>
<h1>Imputation Evaluation</h1>
<p></p>
<h3><b>Table 1L:</b> The sensitivity (SEN), specificity (SPE),
positive predictive value (PPV), negative predictive value (NPV)
and call rate (CR).</h3>
<table id="TB-Acc" class="tabular" border="1"  CELLSPACING="1">
<tr>
<th>Allele </th> <th>Num. Train</th> <th>Freq. Train</th> <th>Num. Valid.</th> <th>Freq. Valid.</th> <th>CR (%)</th> <th>ACC (%)</th> <th>SEN (%)</th> <th>SPE (%)</th> <th>PPV (%)</th> <th>NPV (%)</th> <th>Miscall (%)</th>
</tr>
<tr>
<td colspan="12">
<i> Overall accuracy: 94.2%, Call rate: 100.0% </i>
</td>
</tr>
<tr>
<td>01:01</td> <td>13</td> <td>0.1912</td> <td>12</td> <td>0.2308</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:01</td> <td>23</td> <td>0.3382</td> <td>20</td> <td>0.3846</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>02:06</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>03:01</td> <td>4</td> <td>0.0588</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>11:01</td> <td>3</td> <td>0.0441</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>98.0</td> <td>66.7</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>23:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>98.1</td> <td>24:02 (100)</td>
</tr>
<tr>
<td>24:02</td> <td>6</td> <td>0.0882</td> <td>5</td> <td>0.0962</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>97.9</td> <td>83.3</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>24:03</td> <td>1</td> <td>0.0147</td> <td>0</td> <td>0</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td> <td>--</td>
</tr>
<tr>
<td>25:01</td> <td>3</td> <td>0.0441</td> <td>2</td> <td>0.0385</td> <td>100.0</td> <td>98.1</td> <td>50.0</td> <td>100.0</td> <td>100.0</td> <td>98.0</td> <td>26:01 (100)</td>
</tr>
<tr>
<td>26:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>100.0</td> <td>98.0</td> <td>50.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>29:02</td> <td>3</td> <td>0.0441</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>31:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>32:01</td> <td>3</td> <td>0.0441</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>100.0</td> <td>--</td>
</tr>
<tr>
<td>68:01</td> <td>2</td> <td>0.0294</td> <td>1</td> <td>0.0192</td> <td>100.0</td> <td>98.1</td> <td>0.0</td> <td>100.0</td> <td>--</td> <td>98.1</td> <td>11:01 (100)</td>
</tr>
</table>

</body>
</html>
**Overall accuracy: 94.2%, Call rate: 100.0%**

| Allele  | # Train | Freq. Train | # Valid. | Freq. Valid. | CR (%) | ACC (%) | SEN (%) | SPE (%) | PPV (%) | NPV (%) | Miscall (%) |
|:--|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|:--|
| 01:01 | 13 | 0.1912 | 12 | 0.2308 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 02:01 | 23 | 0.3382 | 20 | 0.3846 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 02:06 |  1 | 0.0147 |  0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 03:01 |  4 | 0.0588 |  5 | 0.0962 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 11:01 |  3 | 0.0441 |  2 | 0.0385 | 100.0 | 98.1 | 100.0 | 98.0 | 66.7 | 100.0 | -- |
| 23:01 |  2 | 0.0294 |  1 | 0.0192 | 100.0 | 98.1 | 0.0 | 100.0 | -- | 98.1 | 24:02 (100) |
| 24:02 |  6 | 0.0882 |  5 | 0.0962 | 100.0 | 98.1 | 100.0 | 97.9 | 83.3 | 100.0 | -- |
| 24:03 |  1 | 0.0147 |  0 | 0 | -- | -- | -- | -- | -- | -- | -- |
| 25:01 |  3 | 0.0441 |  2 | 0.0385 | 100.0 | 98.1 | 50.0 | 100.0 | 100.0 | 98.0 | 26:01 (100) |
| 26:01 |  2 | 0.0294 |  1 | 0.0192 | 100.0 | 98.1 | 100.0 | 98.0 | 50.0 | 100.0 | -- |
| 29:02 |  3 | 0.0441 |  1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 31:01 |  2 | 0.0294 |  1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 32:01 |  3 | 0.0441 |  1 | 0.0192 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | 100.0 | -- |
| 68:01 |  2 | 0.0294 |  1 | 0.0192 | 100.0 | 98.1 | 0.0 | 100.0 | -- | 98.1 | 11:01 (100) |
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:10
     1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
     2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
     3, SNP: 106, Loss: 123.308, OOB Acc: 77.27%, # of Haplo: 16
     4, SNP: 87, Loss: 77.8709, OOB Acc: 81.82%, # of Haplo: 20
     5, SNP: 114, Loss: 43.8425, OOB Acc: 90.91%, # of Haplo: 20
     6, SNP: 7, Loss: 36.5344, OOB Acc: 90.91%, # of Haplo: 23
     7, SNP: 199, Loss: 26.705, OOB Acc: 90.91%, # of Haplo: 24
     8, SNP: 201, Loss: 16.5421, OOB Acc: 90.91%, # of Haplo: 26
     9, SNP: 102, Loss: 15.7295, OOB Acc: 90.91%, # of Haplo: 33
    10, SNP: 144, Loss: 15.078, OOB Acc: 90.91%, # of Haplo: 33
    11, SNP: 52, Loss: 14.8361, OOB Acc: 90.91%, # of Haplo: 42
    12, SNP: 108, Loss: 14.7598, OOB Acc: 90.91%, # of Haplo: 42
    13, SNP: 73, Loss: 11.7396, OOB Acc: 90.91%, # of Haplo: 43
    14, SNP: 151, Loss: 11.3074, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:04:11, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 45
     1, SNP: 45, Loss: 180.067, OOB Acc: 50.00%, # of Haplo: 15
     2, SNP: 75, Loss: 135.995, OOB Acc: 60.71%, # of Haplo: 18
     3, SNP: 176, Loss: 100.071, OOB Acc: 64.29%, # of Haplo: 24
     4, SNP: 181, Loss: 72.877, OOB Acc: 67.86%, # of Haplo: 34
     5, SNP: 154, Loss: 60.9088, OOB Acc: 71.43%, # of Haplo: 35
     6, SNP: 180, Loss: 56.0063, OOB Acc: 71.43%, # of Haplo: 39
     7, SNP: 164, Loss: 51.6779, OOB Acc: 71.43%, # of Haplo: 48
     8, SNP: 227, Loss: 47.3381, OOB Acc: 71.43%, # of Haplo: 50
     9, SNP: 197, Loss: 40.1255, OOB Acc: 71.43%, # of Haplo: 50
    10, SNP: 165, Loss: 38.8164, OOB Acc: 71.43%, # of Haplo: 49
    11, SNP: 151, Loss: 34.5811, OOB Acc: 71.43%, # of Haplo: 49
    12, SNP: 222, Loss: 34.3146, OOB Acc: 71.43%, # of Haplo: 53
    13, SNP: 111, Loss: 34.3145, OOB Acc: 75.00%, # of Haplo: 61
    14, SNP: 73, Loss: 27.4168, OOB Acc: 75.00%, # of Haplo: 61
    15, SNP: 132, Loss: 27.3856, OOB Acc: 75.00%, # of Haplo: 61
    16, SNP: 199, Loss: 25.7831, OOB Acc: 75.00%, # of Haplo: 61
[2] 2019-04-13 03:04:11, OOB Acc: 75.00%, # of SNPs: 16, # of Haplo: 61
     1, SNP: 119, Loss: 233.718, OOB Acc: 50.00%, # of Haplo: 13
     2, SNP: 232, Loss: 181.18, OOB Acc: 52.78%, # of Haplo: 16
     3, SNP: 71, Loss: 139.471, OOB Acc: 58.33%, # of Haplo: 18
     4, SNP: 166, Loss: 116.525, OOB Acc: 61.11%, # of Haplo: 20
     5, SNP: 116, Loss: 87.4554, OOB Acc: 63.89%, # of Haplo: 24
     6, SNP: 126, Loss: 42.9667, OOB Acc: 66.67%, # of Haplo: 30
     7, SNP: 33, Loss: 32.6021, OOB Acc: 66.67%, # of Haplo: 41
     8, SNP: 102, Loss: 30.9058, OOB Acc: 69.44%, # of Haplo: 41
     9, SNP: 169, Loss: 30.4075, OOB Acc: 72.22%, # of Haplo: 41
    10, SNP: 148, Loss: 29.993, OOB Acc: 72.22%, # of Haplo: 41
    11, SNP: 87, Loss: 29.5166, OOB Acc: 72.22%, # of Haplo: 41
    12, SNP: 70, Loss: 24.11, OOB Acc: 72.22%, # of Haplo: 41
    13, SNP: 151, Loss: 23.5686, OOB Acc: 72.22%, # of Haplo: 42
[3] 2019-04-13 03:04:11, OOB Acc: 72.22%, # of SNPs: 13, # of Haplo: 42
     1, SNP: 102, Loss: 142.289, OOB Acc: 38.46%, # of Haplo: 15
     2, SNP: 75, Loss: 110.14, OOB Acc: 46.15%, # of Haplo: 19
     3, SNP: 214, Loss: 91.7293, OOB Acc: 53.85%, # of Haplo: 19
     4, SNP: 121, Loss: 63.0626, OOB Acc: 57.69%, # of Haplo: 19
     5, SNP: 194, Loss: 34.2678, OOB Acc: 57.69%, # of Haplo: 23
     6, SNP: 203, Loss: 21.9942, OOB Acc: 57.69%, # of Haplo: 28
     7, SNP: 130, Loss: 18.122, OOB Acc: 61.54%, # of Haplo: 36
     8, SNP: 14, Loss: 12.615, OOB Acc: 61.54%, # of Haplo: 38
     9, SNP: 17, Loss: 13.5634, OOB Acc: 65.38%, # of Haplo: 41
    10, SNP: 122, Loss: 12.8134, OOB Acc: 65.38%, # of Haplo: 41
    11, SNP: 199, Loss: 12.3319, OOB Acc: 65.38%, # of Haplo: 41
    12, SNP: 134, Loss: 11.1247, OOB Acc: 65.38%, # of Haplo: 41
    13, SNP: 128, Loss: 10.7586, OOB Acc: 65.38%, # of Haplo: 41
    14, SNP: 223, Loss: 10.3119, OOB Acc: 65.38%, # of Haplo: 42
    15, SNP: 37, Loss: 10.0841, OOB Acc: 65.38%, # of Haplo: 44
    16, SNP: 185, Loss: 9.84428, OOB Acc: 65.38%, # of Haplo: 44
    17, SNP: 151, Loss: 9.21691, OOB Acc: 65.38%, # of Haplo: 44
    18, SNP: 73, Loss: 7.59606, OOB Acc: 65.38%, # of Haplo: 46
    19, SNP: 127, Loss: 7.59606, OOB Acc: 69.23%, # of Haplo: 46
    20, SNP: 110, Loss: 7.59606, OOB Acc: 73.08%, # of Haplo: 46
    21, SNP: 54, Loss: 9.02042, OOB Acc: 76.92%, # of Haplo: 52
    22, SNP: 184, Loss: 7.59607, OOB Acc: 76.92%, # of Haplo: 52
[4] 2019-04-13 03:04:11, OOB Acc: 76.92%, # of SNPs: 22, # of Haplo: 52
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002162630 0.0002210881 0.0002645141 0.0039247319 0.0075458074 0.0145272758 
        Max.         Mean           SD 
0.4532643335 0.0352737673 0.1063114572 
Accuracy with training data: 94.1%
Out-of-bag accuracy: 78.8%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 54
    avg. # of SNPs in an individual classifier: 16.25
        (sd: 4.03, min: 13, max: 22, median: 15.00)
    avg. # of haplotypes in an individual classifier: 50.00
        (sd: 8.45, min: 42, max: 61, median: 48.50)
    avg. out-of-bag accuracy: 78.76%
        (sd: 8.32%, min: 72.22%, max: 90.91%, median: 75.96%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002162630 0.0002210881 0.0002645141 0.0039247319 0.0075458074 0.0145272758 
        Max.         Mean           SD 
0.4532643335 0.0352737673 0.1063114572 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:04:11)	0%
Predicting (2019-04-13 03:04:11)	100%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 8
# of SNPs: 51, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:04:11
     1, SNP: 13, Loss: 391.274, OOB Acc: 41.67%, # of Haplo: 17
     2, SNP: 2, Loss: 321.685, OOB Acc: 52.08%, # of Haplo: 18
     3, SNP: 36, Loss: 232.846, OOB Acc: 58.33%, # of Haplo: 19
     4, SNP: 28, Loss: 178.077, OOB Acc: 62.50%, # of Haplo: 20
     5, SNP: 35, Loss: 107.151, OOB Acc: 68.75%, # of Haplo: 20
     6, SNP: 3, Loss: 72.2736, OOB Acc: 72.92%, # of Haplo: 23
     7, SNP: 19, Loss: 50.8439, OOB Acc: 77.08%, # of Haplo: 25
     8, SNP: 4, Loss: 47.2744, OOB Acc: 83.33%, # of Haplo: 29
     9, SNP: 42, Loss: 47.0092, OOB Acc: 85.42%, # of Haplo: 37
    10, SNP: 33, Loss: 41.5486, OOB Acc: 85.42%, # of Haplo: 41
    11, SNP: 5, Loss: 39.769, OOB Acc: 85.42%, # of Haplo: 51
    12, SNP: 10, Loss: 34.0977, OOB Acc: 85.42%, # of Haplo: 51
    13, SNP: 37, Loss: 32.3969, OOB Acc: 85.42%, # of Haplo: 52
    14, SNP: 7, Loss: 28.1492, OOB Acc: 85.42%, # of Haplo: 52
    15, SNP: 15, Loss: 27.2163, OOB Acc: 85.42%, # of Haplo: 55
[1] 2019-04-13 03:04:11, OOB Acc: 85.42%, # of SNPs: 15, # of Haplo: 55
     1, SNP: 18, Loss: 453.852, OOB Acc: 44.12%, # of Haplo: 17
     2, SNP: 4, Loss: 358.517, OOB Acc: 50.00%, # of Haplo: 18
     3, SNP: 49, Loss: 258.495, OOB Acc: 52.94%, # of Haplo: 18
     4, SNP: 5, Loss: 172.555, OOB Acc: 67.65%, # of Haplo: 21
     5, SNP: 42, Loss: 144.905, OOB Acc: 76.47%, # of Haplo: 21
     6, SNP: 38, Loss: 98.7462, OOB Acc: 79.41%, # of Haplo: 21
     7, SNP: 36, Loss: 83.4743, OOB Acc: 82.35%, # of Haplo: 24
     8, SNP: 19, Loss: 60.2385, OOB Acc: 88.24%, # of Haplo: 24
     9, SNP: 46, Loss: 49.1775, OOB Acc: 88.24%, # of Haplo: 24
    10, SNP: 20, Loss: 42.3205, OOB Acc: 88.24%, # of Haplo: 24
    11, SNP: 12, Loss: 41.1299, OOB Acc: 91.18%, # of Haplo: 25
    12, SNP: 1, Loss: 33.8332, OOB Acc: 91.18%, # of Haplo: 25
    13, SNP: 37, Loss: 32.8313, OOB Acc: 91.18%, # of Haplo: 26
    14, SNP: 7, Loss: 38.8398, OOB Acc: 94.12%, # of Haplo: 27
    15, SNP: 15, Loss: 35.0817, OOB Acc: 94.12%, # of Haplo: 32
    16, SNP: 39, Loss: 33.7063, OOB Acc: 94.12%, # of Haplo: 30
[2] 2019-04-13 03:04:11, OOB Acc: 94.12%, # of SNPs: 16, # of Haplo: 30
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
3.479253e-10 4.700783e-10 1.569456e-09 3.197938e-03 1.266674e-02 3.773631e-02 
        Max.         Mean           SD 
9.805734e-02 2.430678e-02 2.697855e-02 
Accuracy with training data: 95.8%
Out-of-bag accuracy: 89.8%
Gene: C
Training dataset: 60 samples X 51 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 2
    total # of SNPs used: 23
    avg. # of SNPs in an individual classifier: 15.50
        (sd: 0.71, min: 15, max: 16, median: 15.50)
    avg. # of haplotypes in an individual classifier: 42.50
        (sd: 17.68, min: 30, max: 55, median: 42.50)
    avg. out-of-bag accuracy: 89.77%
        (sd: 6.15%, min: 85.42%, max: 94.12%, median: 89.77%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
3.479253e-10 4.700783e-10 1.569456e-09 3.197938e-03 1.266674e-02 3.773631e-02 
        Max.         Mean           SD 
9.805734e-02 2.430678e-02 2.697855e-02 
Genome assembly: hg19
Gene: C
Training dataset: 60 samples X 51 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 1
    total # of SNPs used: 15
    avg. # of SNPs in an individual classifier: 15.00
        (sd: NA, min: 15, max: 15, median: 15.00)
    avg. # of haplotypes in an individual classifier: 55.00
        (sd: NA, min: 55, max: 55, median: 55.00)
    avg. out-of-bag accuracy: 85.42%
        (sd: NA%, min: 85.42%, max: 85.42%, median: 85.42%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
3.479253e-10 4.700783e-10 1.569456e-09 3.197938e-03 1.266674e-02 3.773631e-02 
        Max.         Mean           SD 
9.805734e-02 2.430678e-02 2.697855e-02 
Genome assembly: hg19
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 60
# of unique HLA alleles: 14
# of unique HLA genotypes: 29
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:04:11
     1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17
     2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17
     3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20
     4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20
     5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22
     6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24
     7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24
     8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22
     9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24
    10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24
    11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28
    12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29
    13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37
    14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38
    15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39
    16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40
    17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41
    18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43
    19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43
[1] 2019-04-13 03:04:11, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43
     1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19
     2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21
     3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21
     4, SNP: 59, Loss: 209.932, OOB Acc: 73.53%, # of Haplo: 21
     5, SNP: 69, Loss: 146.631, OOB Acc: 82.35%, # of Haplo: 21
     6, SNP: 73, Loss: 96.4111, OOB Acc: 91.18%, # of Haplo: 21
     7, SNP: 70, Loss: 81.5466, OOB Acc: 91.18%, # of Haplo: 21
     8, SNP: 3, Loss: 71.8294, OOB Acc: 91.18%, # of Haplo: 22
     9, SNP: 79, Loss: 63.7214, OOB Acc: 94.12%, # of Haplo: 26
    10, SNP: 5, Loss: 59.4963, OOB Acc: 94.12%, # of Haplo: 27
    11, SNP: 27, Loss: 39.6101, OOB Acc: 94.12%, # of Haplo: 27
    12, SNP: 15, Loss: 39.0565, OOB Acc: 94.12%, # of Haplo: 27
    13, SNP: 56, Loss: 32.0611, OOB Acc: 94.12%, # of Haplo: 30
    14, SNP: 2, Loss: 28.065, OOB Acc: 94.12%, # of Haplo: 33
    15, SNP: 6, Loss: 26.52, OOB Acc: 94.12%, # of Haplo: 37
    16, SNP: 81, Loss: 26.4461, OOB Acc: 94.12%, # of Haplo: 43
    17, SNP: 82, Loss: 25.6218, OOB Acc: 94.12%, # of Haplo: 51
    18, SNP: 32, Loss: 20.8498, OOB Acc: 94.12%, # of Haplo: 52
[2] 2019-04-13 03:04:12, OOB Acc: 94.12%, # of SNPs: 18, # of Haplo: 52
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.361139e-08 7.194017e-06 7.172766e-05 1.579874e-03 9.379945e-03 3.397539e-02 
        Max.         Mean           SD 
7.489335e-02 1.870792e-02 2.126647e-02 
Accuracy with training data: 96.7%
Out-of-bag accuracy: 91.9%
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 34
# of unique HLA alleles: 14
# of unique HLA genotypes: 21
Gene: A
Range: [29910247bp, 29913661bp] on hg19
# of samples: 26
# of unique HLA alleles: 12
# of unique HLA genotypes: 15
Exclude 9 monomorphic SNPs
Build a HIBAG model with 4 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 34
# of unique HLA alleles: 14
[-] 2019-04-13 03:04:12
     1, SNP: 123, Loss: 203.122, OOB Acc: 54.55%, # of Haplo: 14
     2, SNP: 182, Loss: 155.176, OOB Acc: 63.64%, # of Haplo: 16
     3, SNP: 106, Loss: 123.308, OOB Acc: 77.27%, # of Haplo: 16
     4, SNP: 87, Loss: 77.8709, OOB Acc: 81.82%, # of Haplo: 20
     5, SNP: 114, Loss: 43.8425, OOB Acc: 90.91%, # of Haplo: 20
     6, SNP: 7, Loss: 36.5344, OOB Acc: 90.91%, # of Haplo: 23
     7, SNP: 199, Loss: 26.705, OOB Acc: 90.91%, # of Haplo: 24
     8, SNP: 201, Loss: 16.5421, OOB Acc: 90.91%, # of Haplo: 26
     9, SNP: 102, Loss: 15.7295, OOB Acc: 90.91%, # of Haplo: 33
    10, SNP: 144, Loss: 15.078, OOB Acc: 90.91%, # of Haplo: 33
    11, SNP: 52, Loss: 14.8361, OOB Acc: 90.91%, # of Haplo: 42
    12, SNP: 108, Loss: 14.7598, OOB Acc: 90.91%, # of Haplo: 42
    13, SNP: 73, Loss: 11.7396, OOB Acc: 90.91%, # of Haplo: 43
    14, SNP: 151, Loss: 11.3074, OOB Acc: 90.91%, # of Haplo: 45
[1] 2019-04-13 03:04:12, OOB Acc: 90.91%, # of SNPs: 14, # of Haplo: 45
     1, SNP: 45, Loss: 180.067, OOB Acc: 50.00%, # of Haplo: 15
     2, SNP: 75, Loss: 135.995, OOB Acc: 60.71%, # of Haplo: 18
     3, SNP: 176, Loss: 100.071, OOB Acc: 64.29%, # of Haplo: 24
     4, SNP: 181, Loss: 72.877, OOB Acc: 67.86%, # of Haplo: 34
     5, SNP: 154, Loss: 60.9088, OOB Acc: 71.43%, # of Haplo: 35
     6, SNP: 180, Loss: 56.0063, OOB Acc: 71.43%, # of Haplo: 39
     7, SNP: 164, Loss: 51.6779, OOB Acc: 71.43%, # of Haplo: 48
     8, SNP: 227, Loss: 47.3381, OOB Acc: 71.43%, # of Haplo: 50
     9, SNP: 197, Loss: 40.1255, OOB Acc: 71.43%, # of Haplo: 50
    10, SNP: 165, Loss: 38.8164, OOB Acc: 71.43%, # of Haplo: 49
    11, SNP: 151, Loss: 34.5811, OOB Acc: 71.43%, # of Haplo: 49
    12, SNP: 222, Loss: 34.3146, OOB Acc: 71.43%, # of Haplo: 53
    13, SNP: 111, Loss: 34.3145, OOB Acc: 75.00%, # of Haplo: 61
    14, SNP: 73, Loss: 27.4168, OOB Acc: 75.00%, # of Haplo: 61
    15, SNP: 132, Loss: 27.3856, OOB Acc: 75.00%, # of Haplo: 61
    16, SNP: 199, Loss: 25.7831, OOB Acc: 75.00%, # of Haplo: 61
[2] 2019-04-13 03:04:12, OOB Acc: 75.00%, # of SNPs: 16, # of Haplo: 61
     1, SNP: 119, Loss: 233.718, OOB Acc: 50.00%, # of Haplo: 13
     2, SNP: 232, Loss: 181.18, OOB Acc: 52.78%, # of Haplo: 16
     3, SNP: 71, Loss: 139.471, OOB Acc: 58.33%, # of Haplo: 18
     4, SNP: 166, Loss: 116.525, OOB Acc: 61.11%, # of Haplo: 20
     5, SNP: 116, Loss: 87.4554, OOB Acc: 63.89%, # of Haplo: 24
     6, SNP: 126, Loss: 42.9667, OOB Acc: 66.67%, # of Haplo: 30
     7, SNP: 33, Loss: 32.6021, OOB Acc: 66.67%, # of Haplo: 41
     8, SNP: 102, Loss: 30.9058, OOB Acc: 69.44%, # of Haplo: 41
     9, SNP: 169, Loss: 30.4075, OOB Acc: 72.22%, # of Haplo: 41
    10, SNP: 148, Loss: 29.993, OOB Acc: 72.22%, # of Haplo: 41
    11, SNP: 87, Loss: 29.5166, OOB Acc: 72.22%, # of Haplo: 41
    12, SNP: 70, Loss: 24.11, OOB Acc: 72.22%, # of Haplo: 41
    13, SNP: 151, Loss: 23.5686, OOB Acc: 72.22%, # of Haplo: 42
[3] 2019-04-13 03:04:12, OOB Acc: 72.22%, # of SNPs: 13, # of Haplo: 42
     1, SNP: 102, Loss: 142.289, OOB Acc: 38.46%, # of Haplo: 15
     2, SNP: 75, Loss: 110.14, OOB Acc: 46.15%, # of Haplo: 19
     3, SNP: 214, Loss: 91.7293, OOB Acc: 53.85%, # of Haplo: 19
     4, SNP: 121, Loss: 63.0626, OOB Acc: 57.69%, # of Haplo: 19
     5, SNP: 194, Loss: 34.2678, OOB Acc: 57.69%, # of Haplo: 23
     6, SNP: 203, Loss: 21.9942, OOB Acc: 57.69%, # of Haplo: 28
     7, SNP: 130, Loss: 18.122, OOB Acc: 61.54%, # of Haplo: 36
     8, SNP: 14, Loss: 12.615, OOB Acc: 61.54%, # of Haplo: 38
     9, SNP: 17, Loss: 13.5634, OOB Acc: 65.38%, # of Haplo: 41
    10, SNP: 122, Loss: 12.8134, OOB Acc: 65.38%, # of Haplo: 41
    11, SNP: 199, Loss: 12.3319, OOB Acc: 65.38%, # of Haplo: 41
    12, SNP: 134, Loss: 11.1247, OOB Acc: 65.38%, # of Haplo: 41
    13, SNP: 128, Loss: 10.7586, OOB Acc: 65.38%, # of Haplo: 41
    14, SNP: 223, Loss: 10.3119, OOB Acc: 65.38%, # of Haplo: 42
    15, SNP: 37, Loss: 10.0841, OOB Acc: 65.38%, # of Haplo: 44
    16, SNP: 185, Loss: 9.84428, OOB Acc: 65.38%, # of Haplo: 44
    17, SNP: 151, Loss: 9.21691, OOB Acc: 65.38%, # of Haplo: 44
    18, SNP: 73, Loss: 7.59606, OOB Acc: 65.38%, # of Haplo: 46
    19, SNP: 127, Loss: 7.59606, OOB Acc: 69.23%, # of Haplo: 46
    20, SNP: 110, Loss: 7.59606, OOB Acc: 73.08%, # of Haplo: 46
    21, SNP: 54, Loss: 9.02042, OOB Acc: 76.92%, # of Haplo: 52
    22, SNP: 184, Loss: 7.59607, OOB Acc: 76.92%, # of Haplo: 52
[4] 2019-04-13 03:04:12, OOB Acc: 76.92%, # of SNPs: 22, # of Haplo: 52
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002162630 0.0002210881 0.0002645141 0.0039247319 0.0075458074 0.0145272758 
        Max.         Mean           SD 
0.4532643335 0.0352737673 0.1063114572 
Accuracy with training data: 94.1%
Out-of-bag accuracy: 78.8%
Gene: A
Training dataset: 34 samples X 266 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 4
    total # of SNPs used: 54
    avg. # of SNPs in an individual classifier: 16.25
        (sd: 4.03, min: 13, max: 22, median: 15.00)
    avg. # of haplotypes in an individual classifier: 50.00
        (sd: 8.45, min: 42, max: 61, median: 48.50)
    avg. out-of-bag accuracy: 78.76%
        (sd: 8.32%, min: 72.22%, max: 90.91%, median: 75.96%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002162630 0.0002210881 0.0002645141 0.0039247319 0.0075458074 0.0145272758 
        Max.         Mean           SD 
0.4532643335 0.0352737673 0.1063114572 
Genome assembly: hg19
HIBAG model: 4 individual classifiers, 266 SNPs, 14 unique HLA alleles.
Predicting based on the averaged posterior probabilities from all individual classifiers.
Model assembly: hg19, SNP assembly: hg19
No allelic strand orders are switched.
Number of samples: 26
Predicting (2019-04-13 03:04:12)	0%
Predicting (2019-04-13 03:04:12)	100%
Build a HIBAG model with 2 individual classifiers:
# of SNPs randomly sampled as candidates for each selection: 10
# of SNPs: 83, # of samples: 60
# of unique HLA alleles: 17
[-] 2019-04-13 03:04:12
     1, SNP: 44, Loss: 396.506, OOB Acc: 41.67%, # of Haplo: 17
     2, SNP: 58, Loss: 291.148, OOB Acc: 50.00%, # of Haplo: 17
     3, SNP: 80, Loss: 211.469, OOB Acc: 56.25%, # of Haplo: 20
     4, SNP: 18, Loss: 138.615, OOB Acc: 60.42%, # of Haplo: 20
     5, SNP: 29, Loss: 111.977, OOB Acc: 62.50%, # of Haplo: 22
     6, SNP: 62, Loss: 90.976, OOB Acc: 68.75%, # of Haplo: 24
     7, SNP: 13, Loss: 70.2962, OOB Acc: 72.92%, # of Haplo: 24
     8, SNP: 14, Loss: 54.5685, OOB Acc: 77.08%, # of Haplo: 22
     9, SNP: 72, Loss: 35.1951, OOB Acc: 77.08%, # of Haplo: 24
    10, SNP: 3, Loss: 23.2868, OOB Acc: 79.17%, # of Haplo: 24
    11, SNP: 70, Loss: 21.089, OOB Acc: 79.17%, # of Haplo: 28
    12, SNP: 5, Loss: 20.9664, OOB Acc: 79.17%, # of Haplo: 29
    13, SNP: 40, Loss: 20.9662, OOB Acc: 83.33%, # of Haplo: 37
    14, SNP: 24, Loss: 20.2385, OOB Acc: 85.42%, # of Haplo: 38
    15, SNP: 2, Loss: 20.2383, OOB Acc: 87.50%, # of Haplo: 39
    16, SNP: 74, Loss: 20.0601, OOB Acc: 87.50%, # of Haplo: 40
    17, SNP: 57, Loss: 17.8846, OOB Acc: 87.50%, # of Haplo: 41
    18, SNP: 56, Loss: 14.7377, OOB Acc: 89.58%, # of Haplo: 43
    19, SNP: 27, Loss: 10.7709, OOB Acc: 89.58%, # of Haplo: 43
[1] 2019-04-13 03:04:12, OOB Acc: 89.58%, # of SNPs: 19, # of Haplo: 43
     1, SNP: 66, Loss: 434.369, OOB Acc: 44.12%, # of Haplo: 19
     2, SNP: 28, Loss: 337.451, OOB Acc: 58.82%, # of Haplo: 21
     3, SNP: 30, Loss: 302.194, OOB Acc: 73.53%, # of Haplo: 21
     4, SNP: 59, Loss: 209.932, OOB Acc: 73.53%, # of Haplo: 21
     5, SNP: 69, Loss: 146.631, OOB Acc: 82.35%, # of Haplo: 21
     6, SNP: 73, Loss: 96.4111, OOB Acc: 91.18%, # of Haplo: 21
     7, SNP: 70, Loss: 81.5466, OOB Acc: 91.18%, # of Haplo: 21
     8, SNP: 3, Loss: 71.8294, OOB Acc: 91.18%, # of Haplo: 22
     9, SNP: 79, Loss: 63.7214, OOB Acc: 94.12%, # of Haplo: 26
    10, SNP: 5, Loss: 59.4963, OOB Acc: 94.12%, # of Haplo: 27
    11, SNP: 27, Loss: 39.6101, OOB Acc: 94.12%, # of Haplo: 27
    12, SNP: 15, Loss: 39.0565, OOB Acc: 94.12%, # of Haplo: 27
    13, SNP: 56, Loss: 32.0611, OOB Acc: 94.12%, # of Haplo: 30
    14, SNP: 2, Loss: 28.065, OOB Acc: 94.12%, # of Haplo: 33
    15, SNP: 6, Loss: 26.52, OOB Acc: 94.12%, # of Haplo: 37
    16, SNP: 81, Loss: 26.4461, OOB Acc: 94.12%, # of Haplo: 43
    17, SNP: 82, Loss: 25.6218, OOB Acc: 94.12%, # of Haplo: 51
    18, SNP: 32, Loss: 20.8498, OOB Acc: 94.12%, # of Haplo: 52
[2] 2019-04-13 03:04:12, OOB Acc: 94.12%, # of SNPs: 18, # of Haplo: 52
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.361139e-08 7.194017e-06 7.172766e-05 1.579874e-03 9.379945e-03 3.397539e-02 
        Max.         Mean           SD 
7.489335e-02 1.870792e-02 2.126647e-02 
Accuracy with training data: 96.7%
Out-of-bag accuracy: 91.9%
Gene: C
Training dataset: 60 samples X 83 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 2
    total # of SNPs used: 31
    avg. # of SNPs in an individual classifier: 18.50
        (sd: 0.71, min: 18, max: 19, median: 18.50)
    avg. # of haplotypes in an individual classifier: 47.50
        (sd: 6.36, min: 43, max: 52, median: 47.50)
    avg. out-of-bag accuracy: 91.85%
        (sd: 3.21%, min: 89.58%, max: 94.12%, median: 91.85%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
2.361139e-08 7.194017e-06 7.172766e-05 1.579874e-03 9.379945e-03 3.397539e-02 
        Max.         Mean           SD 
7.489335e-02 1.870792e-02 2.126647e-02 
Genome assembly: hg19
SNP genotypes: 
    60 samples X 1564 SNPs
    SNPs range from 25769023bp to 33421576bp on hg19
Missing rate per SNP:
    min: 0, max: 0.0666667, mean: 0.0651215, median: 0.0666667, sd: 0.00968287
Missing rate per sample:
    min: 0, max: 0.969949, mean: 0.0651215, median: 0.000639386, sd: 0.243737
Minor allele frequency:
    min: 0, max: 0.5, mean: 0.227582, median: 0.205357, sd: 0.1389
Allelic information:
A/G C/T G/T A/C 
655 632 141 136 
> 
> proc.time()
   user  system elapsed 
  23.10    0.42   25.53 

Example timings

HIBAG.Rcheck/examples_i386/HIBAG-Ex.timings

nameusersystemelapsed
HIBAG-package0.570.001.08
hlaAllele000
hlaAlleleDigit0.020.000.02
hlaAlleleSubset000
hlaAssocTest0.700.020.72
hlaAttrBagging0.510.000.51
hlaBED2Geno0.080.000.08
hlaCheckAllele000
hlaCheckSNPs0.080.000.08
hlaCombineAllele0.020.000.01
hlaCombineModelObj0.400.000.41
hlaCompareAllele0.470.000.47
hlaConvSequence2.560.192.81
hlaDistance2.560.002.56
hlaErrMsg000
hlaFlankingSNP000
hlaGDS2Geno0.080.010.25
hlaGeno2PED0.030.000.03
hlaGenoAFreq000
hlaGenoCombine0.060.000.06
hlaGenoLD0.720.000.72
hlaGenoMFreq0.020.000.02
hlaGenoMRate000
hlaGenoMRate_Samp000
hlaGenoSubset0.010.000.01
hlaGenoSwitchStrand0.050.000.05
hlaLDMatrix1.140.061.20
hlaLociInfo0.000.020.02
hlaMakeSNPGeno0.020.000.01
hlaModelFiles0.230.000.27
hlaModelFromObj0.080.000.08
hlaOutOfBag0.920.010.94
hlaParallelAttrBagging0.130.052.17
hlaPredMerge0.510.000.51
hlaPublish0.530.020.55
hlaReport0.440.010.45
hlaReportPlot1.420.051.47
hlaSNPID000
hlaSampleAllele0.020.000.02
hlaSplitAllele0.030.000.03
hlaSubModelObj0.090.000.09
hlaUniqueAllele000
plot.hlaAttrBagObj0.330.000.33
predict.hlaAttrBagClass0.450.000.45
print.hlaAttrBagClass0.130.030.16
summary.hlaSNPGenoClass000

HIBAG.Rcheck/examples_x64/HIBAG-Ex.timings

nameusersystemelapsed
HIBAG-package0.540.070.75
hlaAllele0.010.000.02
hlaAlleleDigit0.030.000.03
hlaAlleleSubset0.010.000.02
hlaAssocTest0.910.000.91
hlaAttrBagging0.50.00.5
hlaBED2Geno0.080.020.09
hlaCheckAllele000
hlaCheckSNPs0.090.000.09
hlaCombineAllele0.020.000.02
hlaCombineModelObj0.280.000.28
hlaCompareAllele0.470.000.47
hlaConvSequence2.390.062.45
hlaDistance1.580.001.58
hlaErrMsg000
hlaFlankingSNP0.000.020.02
hlaGDS2Geno0.040.010.06
hlaGeno2PED0.030.000.04
hlaGenoAFreq000
hlaGenoCombine0.050.000.04
hlaGenoLD0.840.000.85
hlaGenoMFreq0.020.000.01
hlaGenoMRate000
hlaGenoMRate_Samp0.020.000.02
hlaGenoSubset000
hlaGenoSwitchStrand0.060.000.06
hlaLDMatrix1.690.141.83
hlaLociInfo0.010.000.01
hlaMakeSNPGeno0.030.000.04
hlaModelFiles0.330.010.34
hlaModelFromObj0.130.000.12
hlaOutOfBag0.730.000.74
hlaParallelAttrBagging0.200.032.00
hlaPredMerge0.520.000.51
hlaPublish0.440.000.44
hlaReport0.430.000.44
hlaReportPlot1.690.001.69
hlaSNPID000
hlaSampleAllele000
hlaSplitAllele0.030.000.03
hlaSubModelObj0.100.000.09
hlaUniqueAllele000
plot.hlaAttrBagObj0.310.000.31
predict.hlaAttrBagClass0.420.000.43
print.hlaAttrBagClass0.130.000.12
summary.hlaSNPGenoClass000