Back to Multiple platform build/check report for BioC 3.18:   simplified   long
ABCDEFG[H]IJKLMNOPQRSTUVWXYZ

This page was generated on 2024-03-29 11:36:51 -0400 (Fri, 29 Mar 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 22.04.3 LTS)x86_644.3.3 (2024-02-29) -- "Angel Food Cake" 4669
palomino4Windows Server 2022 Datacenterx644.3.3 (2024-02-29 ucrt) -- "Angel Food Cake" 4404
merida1macOS 12.7.1 Montereyx86_644.3.3 (2024-02-29) -- "Angel Food Cake" 4427
Click on any hostname to see more info about the system (e.g. compilers)      (*) as reported by 'uname -p', except on Windows and Mac OS X

Package 947/2266HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
HIBAG 1.38.2  (landing page)
Xiuwen Zheng
Snapshot Date: 2024-03-27 14:05:05 -0400 (Wed, 27 Mar 2024)
git_url: https://git.bioconductor.org/packages/HIBAG
git_branch: RELEASE_3_18
git_last_commit: a1b52dd
git_last_commit_date: 2024-01-25 01:08:53 -0400 (Thu, 25 Jan 2024)
nebbiolo2Linux (Ubuntu 22.04.3 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
palomino4Windows Server 2022 Datacenter / x64  OK    OK    OK    OK  UNNEEDED, same version is already published
merida1macOS 12.7.1 Monterey / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published
kjohnson1macOS 13.6.1 Ventura / arm64see weekly results here

CHECK results for HIBAG on palomino4


To the developers/maintainers of the HIBAG package:
- Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/HIBAG.git to reflect on this report. See Troubleshooting Build Report for more information.
- Use the following Renviron settings to reproduce errors and warnings.
- If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information.

raw results


Summary

Package: HIBAG
Version: 1.38.2
Command: F:\biocbuild\bbs-3.18-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:HIBAG.install-out.txt --library=F:\biocbuild\bbs-3.18-bioc\R\library --no-vignettes --timings HIBAG_1.38.2.tar.gz
StartedAt: 2024-03-28 01:41:01 -0400 (Thu, 28 Mar 2024)
EndedAt: 2024-03-28 01:42:34 -0400 (Thu, 28 Mar 2024)
EllapsedTime: 93.0 seconds
RetCode: 0
Status:   OK  
CheckDir: HIBAG.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   F:\biocbuild\bbs-3.18-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:HIBAG.install-out.txt --library=F:\biocbuild\bbs-3.18-bioc\R\library --no-vignettes --timings HIBAG_1.38.2.tar.gz
###
##############################################################################
##############################################################################


* using log directory 'F:/biocbuild/bbs-3.18-bioc/meat/HIBAG.Rcheck'
* using R version 4.3.3 (2024-02-29 ucrt)
* using platform: x86_64-w64-mingw32 (64-bit)
* R was compiled by
    gcc.exe (GCC) 12.3.0
    GNU Fortran (GCC) 12.3.0
* running under: Windows Server 2022 x64 (build 20348)
* using session charset: UTF-8
* using option '--no-vignettes'
* checking for file 'HIBAG/DESCRIPTION' ... OK
* checking extension type ... Package
* this is package 'HIBAG' version '1.38.2'
* checking package namespace information ... OK
* checking package dependencies ... 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
* used C compiler: 'gcc.exe (GCC) 12.3.0'
* used C++ compiler: 'G__~1.EXE (GCC) 12.3.0'
* checking C++ specification ... NOTE
  Specified C++11: please drop specification unless essential
* 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
* 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 startup messages can be suppressed ... 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 LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking compilation flags in Makevars ... OK
* checking for GNU extensions in Makefiles ... NOTE
GNU make is a SystemRequirements.
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking use of PKG_*FLAGS in Makefiles ... OK
* checking compiled code ... NOTE
Note: information on .o files for x64 is not available
File 'F:/biocbuild/bbs-3.18-bioc/R/library/HIBAG/libs/x64/HIBAG.dll':
  Found 'abort', possibly from 'abort' (C), 'runtime' (Fortran)
  Found 'exit', possibly from 'exit' (C), 'stop' (Fortran)
File 'HIBAG/libs/x64/HIBAG.dll':
  Found non-API call to R: 'R_new_custom_connection'

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 nor [v]sprintf. The detected symbols are linked into
the code but might come from libraries and not actually be called.
Compiled code should not call non-API entry points in R.

See 'Writing portable packages' in the 'Writing R Extensions' manual.
* checking installed files from 'inst/doc' ... OK
* checking files in 'vignettes' ... OK
* checking examples ... OK
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
  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: 3 NOTEs
See
  'F:/biocbuild/bbs-3.18-bioc/meat/HIBAG.Rcheck/00check.log'
for details.



Installation output

HIBAG.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   F:\biocbuild\bbs-3.18-bioc\R\bin\R.exe CMD INSTALL HIBAG
###
##############################################################################
##############################################################################


* installing to library 'F:/biocbuild/bbs-3.18-bioc/R/library'
* installing *source* package 'HIBAG' ...
** using staged installation
** libs
using C compiler: 'gcc.exe (GCC) 12.3.0'
using C++ compiler: 'G__~1.EXE (GCC) 12.3.0'
using C++11
g++  -std=gnu++11 -I"F:/biocbuild/bbs-3.18-bioc/R/include" -DNDEBUG  -I'F:/biocbuild/bbs-3.18-bioc/R/library/RcppParallel/include'   -I"C:/rtools43/x86_64-w64-mingw32.static.posix/include"  -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include   -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c HIBAG.cpp -o HIBAG.o
g++  -std=gnu++11 -I"F:/biocbuild/bbs-3.18-bioc/R/include" -DNDEBUG  -I'F:/biocbuild/bbs-3.18-bioc/R/library/RcppParallel/include'   -I"C:/rtools43/x86_64-w64-mingw32.static.posix/include"  -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include   -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c LibHLA.cpp -o LibHLA.o
g++  -std=gnu++11 -I"F:/biocbuild/bbs-3.18-bioc/R/include" -DNDEBUG  -I'F:/biocbuild/bbs-3.18-bioc/R/library/RcppParallel/include'   -I"C:/rtools43/x86_64-w64-mingw32.static.posix/include"  -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include   -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c LibHLA_ext_avx.cpp -o LibHLA_ext_avx.o
g++  -std=gnu++11 -I"F:/biocbuild/bbs-3.18-bioc/R/include" -DNDEBUG  -I'F:/biocbuild/bbs-3.18-bioc/R/library/RcppParallel/include'   -I"C:/rtools43/x86_64-w64-mingw32.static.posix/include"  -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include   -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c LibHLA_ext_avx2.cpp -o LibHLA_ext_avx2.o
g++  -std=gnu++11 -I"F:/biocbuild/bbs-3.18-bioc/R/include" -DNDEBUG  -I'F:/biocbuild/bbs-3.18-bioc/R/library/RcppParallel/include'   -I"C:/rtools43/x86_64-w64-mingw32.static.posix/include"  -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include   -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -ffixed-xmm16 -ffixed-xmm17 -ffixed-xmm18 -ffixed-xmm19 -ffixed-xmm20 -ffixed-xmm21 -ffixed-xmm22 -ffixed-xmm23 -ffixed-xmm24 -ffixed-xmm25 -ffixed-xmm26 -ffixed-xmm27 -ffixed-xmm28 -ffixed-xmm29 -ffixed-xmm30 -ffixed-xmm31 LibHLA_ext_avx512bw.cpp -c -o LibHLA_ext_avx512bw.o
g++  -std=gnu++11 -I"F:/biocbuild/bbs-3.18-bioc/R/include" -DNDEBUG  -I'F:/biocbuild/bbs-3.18-bioc/R/library/RcppParallel/include'   -I"C:/rtools43/x86_64-w64-mingw32.static.posix/include"  -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include   -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -ffixed-xmm16 -ffixed-xmm17 -ffixed-xmm18 -ffixed-xmm19 -ffixed-xmm20 -ffixed-xmm21 -ffixed-xmm22 -ffixed-xmm23 -ffixed-xmm24 -ffixed-xmm25 -ffixed-xmm26 -ffixed-xmm27 -ffixed-xmm28 -ffixed-xmm29 -ffixed-xmm30 -ffixed-xmm31 LibHLA_ext_avx512f.cpp -c -o LibHLA_ext_avx512f.o
g++  -std=gnu++11 -I"F:/biocbuild/bbs-3.18-bioc/R/include" -DNDEBUG  -I'F:/biocbuild/bbs-3.18-bioc/R/library/RcppParallel/include'   -I"C:/rtools43/x86_64-w64-mingw32.static.posix/include"  -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include   -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -ffixed-xmm16 -ffixed-xmm17 -ffixed-xmm18 -ffixed-xmm19 -ffixed-xmm20 -ffixed-xmm21 -ffixed-xmm22 -ffixed-xmm23 -ffixed-xmm24 -ffixed-xmm25 -ffixed-xmm26 -ffixed-xmm27 -ffixed-xmm28 -ffixed-xmm29 -ffixed-xmm30 -ffixed-xmm31 LibHLA_ext_avx512vpopcnt.cpp -c -o LibHLA_ext_avx512vpopcnt.o
g++  -std=gnu++11 -I"F:/biocbuild/bbs-3.18-bioc/R/include" -DNDEBUG  -I'F:/biocbuild/bbs-3.18-bioc/R/library/RcppParallel/include'   -I"C:/rtools43/x86_64-w64-mingw32.static.posix/include"  -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include   -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c LibHLA_ext_sse2.cpp -o LibHLA_ext_sse2.o
g++  -std=gnu++11 -I"F:/biocbuild/bbs-3.18-bioc/R/include" -DNDEBUG  -I'F:/biocbuild/bbs-3.18-bioc/R/library/RcppParallel/include'   -I"C:/rtools43/x86_64-w64-mingw32.static.posix/include"  -DRCPP_PARALLEL_USE_TBB=1 -I../inst/include   -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c LibHLA_ext_sse4_2.cpp -o LibHLA_ext_sse4_2.o
gcc  -I"F:/biocbuild/bbs-3.18-bioc/R/include" -DNDEBUG  -I'F:/biocbuild/bbs-3.18-bioc/R/library/RcppParallel/include'   -I"C:/rtools43/x86_64-w64-mingw32.static.posix/include"     -O2 -Wall  -mfpmath=sse -msse2 -mstackrealign  -c samtools_ext.c -o samtools_ext.o
g++ -shared -s -static-libgcc -o HIBAG.dll tmp.def HIBAG.o LibHLA.o LibHLA_ext_avx.o LibHLA_ext_avx2.o LibHLA_ext_avx512bw.o LibHLA_ext_avx512f.o LibHLA_ext_avx512vpopcnt.o LibHLA_ext_sse2.o LibHLA_ext_sse4_2.o samtools_ext.o -LF:/biocbuild/bbs-3.18-bioc/R/library/RcppParallel/lib/x64 -ltbb -ltbbmalloc -LC:/rtools43/x86_64-w64-mingw32.static.posix/lib/x64 -LC:/rtools43/x86_64-w64-mingw32.static.posix/lib -LF:/biocbuild/bbs-3.18-bioc/R/bin/x64 -lR
installing to F:/biocbuild/bbs-3.18-bioc/R/library/00LOCK-HIBAG/00new/HIBAG/libs/x64
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (HIBAG)

Tests output

HIBAG.Rcheck/tests/runTests.Rout


R version 4.3.3 (2024-02-29 ucrt) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-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: v1.5 (64-bit, AVX512BW)
> 
> 
> #############################################################
> 
> # 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, 0.8, 0.8, 0.7)
> 
> 
> for (hla.idx in seq_along(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$training, train.geno, nclassifier=10)
+ 	summary(model)
+ 
+ 	# validation
+ 	pred <- hlaPredict(model, test.geno, type="response")
+ 	summary(pred)
+ 
+ 	# compare
+ 	comp <- hlaCompareAllele(hlatab$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")
+ }
Build a HIBAG model with 10 individual classifiers:
    MAF threshold: NaN
    excluding 11 monomorphic SNPs
    # of SNPs randomly sampled as candidates for each selection: 17
    # of SNPs: 264
    # of samples: 34
    # of unique HLA alleles: 14
CPU flags: 64-bit, AVX512BW
# of threads: 1
[-] 2024-03-28 01:42:14
=== building individual classifier 1, out-of-bag (11/32.4%) ===
[1] 2024-03-28 01:42:14, oob acc: 77.27%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
[2] 2024-03-28 01:42:14, oob acc: 88.46%, # of SNPs: 12, # of haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
[3] 2024-03-28 01:42:14, oob acc: 85.71%, # of SNPs: 11, # of haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
[4] 2024-03-28 01:42:14, oob acc: 75.00%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 5, out-of-bag (17/50.0%) ===
[5] 2024-03-28 01:42:14, oob acc: 79.41%, # of SNPs: 13, # of haplo: 34
=== building individual classifier 6, out-of-bag (11/32.4%) ===
[6] 2024-03-28 01:42:14, oob acc: 100.00%, # of SNPs: 19, # of haplo: 72
=== building individual classifier 7, out-of-bag (9/26.5%) ===
[7] 2024-03-28 01:42:14, oob acc: 100.00%, # of SNPs: 17, # of haplo: 37
=== building individual classifier 8, out-of-bag (13/38.2%) ===
[8] 2024-03-28 01:42:14, oob acc: 88.46%, # of SNPs: 15, # of haplo: 57
=== building individual classifier 9, out-of-bag (15/44.1%) ===
[9] 2024-03-28 01:42:14, oob acc: 93.33%, # of SNPs: 13, # of haplo: 22
=== building individual classifier 10, out-of-bag (13/38.2%) ===
[10] 2024-03-28 01:42:14, oob acc: 80.77%, # of SNPs: 14, # of haplo: 24
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0005514978 0.0005592583 0.0006291033 0.0043053528 0.0091076487 0.0245841398 
        Max.         Mean           SD 
0.4332369854 0.0431831923 0.0993605214 
Accuracy with training data: 98.53%
Out-of-bag accuracy: 86.84%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 10
    total # of SNPs used: 94
    avg. # of SNPs in an individual classifier: 14.00
        (sd: 2.40, min: 11, max: 19, median: 13.00)
    avg. # of haplotypes in an individual classifier: 35.40
        (sd: 18.39, min: 14, max: 72, median: 29.00)
    avg. out-of-bag accuracy: 86.84%
        (sd: 8.94%, min: 75.00%, max: 100.00%, median: 87.09%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0005514978 0.0005592583 0.0006291033 0.0043053528 0.0091076487 0.0245841398 
        Max.         Mean           SD 
0.4332369854 0.0431831923 0.0993605214 
Genome assembly: hg19
HIBAG model for HLA-A:
    10 individual classifiers
    264 SNPs
    14 unique HLA alleles: 01:01, 02:01, 02:06, ...
Prediction:
    based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
    match.type="--"   missing SNPs #                
           Position         0 (0.0%) *being used [1]
         Pos+Allele         0 (0.0%)             [2]
    RefSNP+Position         0 (0.0%)                
             RefSNP         0 (0.0%)                
      [1]: useful if ambiguous strands on array-based platforms
      [2]: suggested if the model and test data have been matched to the same reference genome
    Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 64-bit, AVX512BW
# of threads: 1
Predicting (2024-03-28 01:42:14)	0%
Predicting (2024-03-28 01:42:14)	100%
Gene: HLA-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%)  3 (11.5%)  4 (15.4%) 18 (69.2%) 
Matching proportion of SNP haplotype:
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
0.000000 0.002500 0.007023 0.031426 0.026829 0.433237 
  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


Build a HIBAG model with 10 individual classifiers:
    MAF threshold: NaN
    excluding 1 monomorphic SNP
    # of SNPs randomly sampled as candidates for each selection: 19
    # of SNPs: 340
    # of samples: 28
    # of unique HLA alleles: 22
CPU flags: 64-bit, AVX512BW
# of threads: 1
[-] 2024-03-28 01:42:14
=== building individual classifier 1, out-of-bag (12/42.9%) ===
[1] 2024-03-28 01:42:14, oob acc: 58.33%, # of SNPs: 17, # of haplo: 52
=== building individual classifier 2, out-of-bag (11/39.3%) ===
[2] 2024-03-28 01:42:15, oob acc: 63.64%, # of SNPs: 18, # of haplo: 51
=== building individual classifier 3, out-of-bag (13/46.4%) ===
[3] 2024-03-28 01:42:15, oob acc: 50.00%, # of SNPs: 15, # of haplo: 29
=== building individual classifier 4, out-of-bag (11/39.3%) ===
[4] 2024-03-28 01:42:15, oob acc: 59.09%, # of SNPs: 12, # of haplo: 57
=== building individual classifier 5, out-of-bag (11/39.3%) ===
[5] 2024-03-28 01:42:15, oob acc: 63.64%, # of SNPs: 15, # of haplo: 94
=== building individual classifier 6, out-of-bag (12/42.9%) ===
[6] 2024-03-28 01:42:15, oob acc: 79.17%, # of SNPs: 18, # of haplo: 66
=== building individual classifier 7, out-of-bag (12/42.9%) ===
[7] 2024-03-28 01:42:15, oob acc: 70.83%, # of SNPs: 15, # of haplo: 86
=== building individual classifier 8, out-of-bag (9/32.1%) ===
[8] 2024-03-28 01:42:15, oob acc: 77.78%, # of SNPs: 16, # of haplo: 117
=== building individual classifier 9, out-of-bag (9/32.1%) ===
[9] 2024-03-28 01:42:15, oob acc: 77.78%, # of SNPs: 18, # of haplo: 92
=== building individual classifier 10, out-of-bag (9/32.1%) ===
[10] 2024-03-28 01:42:15, oob acc: 61.11%, # of SNPs: 15, # of haplo: 72
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
9.567411e-05 9.622439e-05 1.011769e-04 3.069782e-03 7.279682e-03 1.186415e-02 
        Max.         Mean           SD 
1.196521e-01 1.281183e-02 2.267335e-02 
Accuracy with training data: 100.00%
Out-of-bag accuracy: 66.14%
Gene: HLA-B
Training dataset: 28 samples X 340 SNPs
    # of HLA alleles: 22
    # of individual classifiers: 10
    total # of SNPs used: 118
    avg. # of SNPs in an individual classifier: 15.90
        (sd: 1.91, min: 12, max: 18, median: 15.50)
    avg. # of haplotypes in an individual classifier: 71.60
        (sd: 25.94, min: 29, max: 117, median: 69.00)
    avg. out-of-bag accuracy: 66.14%
        (sd: 9.84%, min: 50.00%, max: 79.17%, median: 63.64%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
9.567411e-05 9.622439e-05 1.011769e-04 3.069782e-03 7.279682e-03 1.186415e-02 
        Max.         Mean           SD 
1.196521e-01 1.281183e-02 2.267335e-02 
Genome assembly: hg19
HIBAG model for HLA-B:
    10 individual classifiers
    340 SNPs
    22 unique HLA alleles: 07:02, 08:01, 13:02, ...
Prediction:
    based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
    match.type="--"   missing SNPs #                
           Position         0 (0.0%) *being used [1]
         Pos+Allele         0 (0.0%)             [2]
    RefSNP+Position         0 (0.0%)                
             RefSNP         0 (0.0%)                
      [1]: useful if ambiguous strands on array-based platforms
      [2]: suggested if the model and test data have been matched to the same reference genome
    Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 15
CPU flags: 64-bit, AVX512BW
# of threads: 1
Predicting (2024-03-28 01:42:15)	0%
Predicting (2024-03-28 01:42:15)	100%
Gene: HLA-B
Range: [31321649bp, 31324989bp] on hg19
# of samples: 15
# of unique HLA alleles: 9
# of unique HLA genotypes: 12
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
 3 (20.0%)  5 (33.3%)  3 (20.0%)  4 (26.7%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
2.000e-08 4.068e-05 2.934e-03 1.789e-02 6.076e-03 1.326e-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


Build a HIBAG model with 10 individual classifiers:
    MAF threshold: NaN
    excluding 2 monomorphic SNPs
    # of SNPs randomly sampled as candidates for each selection: 19
    # of SNPs: 354
    # of samples: 36
    # of unique HLA alleles: 17
CPU flags: 64-bit, AVX512BW
# of threads: 1
[-] 2024-03-28 01:42:15
=== building individual classifier 1, out-of-bag (13/36.1%) ===
[1] 2024-03-28 01:42:15, oob acc: 80.77%, # of SNPs: 19, # of haplo: 40
=== building individual classifier 2, out-of-bag (11/30.6%) ===
[2] 2024-03-28 01:42:15, oob acc: 90.91%, # of SNPs: 32, # of haplo: 32
=== building individual classifier 3, out-of-bag (14/38.9%) ===
[3] 2024-03-28 01:42:16, oob acc: 89.29%, # of SNPs: 19, # of haplo: 43
=== building individual classifier 4, out-of-bag (13/36.1%) ===
[4] 2024-03-28 01:42:16, oob acc: 84.62%, # of SNPs: 19, # of haplo: 100
=== building individual classifier 5, out-of-bag (9/25.0%) ===
[5] 2024-03-28 01:42:16, oob acc: 94.44%, # of SNPs: 22, # of haplo: 58
=== building individual classifier 6, out-of-bag (17/47.2%) ===
[6] 2024-03-28 01:42:16, oob acc: 79.41%, # of SNPs: 27, # of haplo: 63
=== building individual classifier 7, out-of-bag (12/33.3%) ===
[7] 2024-03-28 01:42:16, oob acc: 75.00%, # of SNPs: 19, # of haplo: 40
=== building individual classifier 8, out-of-bag (13/36.1%) ===
[8] 2024-03-28 01:42:16, oob acc: 80.77%, # of SNPs: 17, # of haplo: 60
=== building individual classifier 9, out-of-bag (11/30.6%) ===
[9] 2024-03-28 01:42:16, oob acc: 86.36%, # of SNPs: 20, # of haplo: 33
=== building individual classifier 10, out-of-bag (10/27.8%) ===
[10] 2024-03-28 01:42:16, oob acc: 90.00%, # of SNPs: 32, # of haplo: 77
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002218427 0.0002248414 0.0002518298 0.0017561996 0.0033916297 0.0102775434 
        Max.         Mean           SD 
0.0909115957 0.0113752369 0.0199373731 
Accuracy with training data: 100.00%
Out-of-bag accuracy: 85.16%
Gene: HLA-C
Training dataset: 36 samples X 354 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 10
    total # of SNPs used: 141
    avg. # of SNPs in an individual classifier: 22.60
        (sd: 5.64, min: 17, max: 32, median: 19.50)
    avg. # of haplotypes in an individual classifier: 54.60
        (sd: 21.63, min: 32, max: 100, median: 50.50)
    avg. out-of-bag accuracy: 85.16%
        (sd: 6.11%, min: 75.00%, max: 94.44%, median: 85.49%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0002218427 0.0002248414 0.0002518298 0.0017561996 0.0033916297 0.0102775434 
        Max.         Mean           SD 
0.0909115957 0.0113752369 0.0199373731 
Genome assembly: hg19
HIBAG model for HLA-C:
    10 individual classifiers
    354 SNPs
    17 unique HLA alleles: 01:02, 02:02, 03:03, ...
Prediction:
    based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
    match.type="--"   missing SNPs #                
           Position         0 (0.0%) *being used [1]
         Pos+Allele         0 (0.0%)             [2]
    RefSNP+Position         0 (0.0%)                
             RefSNP         0 (0.0%)                
      [1]: useful if ambiguous strands on array-based platforms
      [2]: suggested if the model and test data have been matched to the same reference genome
    Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 24
CPU flags: 64-bit, AVX512BW
# of threads: 1
Predicting (2024-03-28 01:42:16)	0%
Predicting (2024-03-28 01:42:16)	100%
Gene: HLA-C
Range: [31236526bp, 31239913bp] on hg19
# of samples: 24
# of unique HLA alleles: 14
# of unique HLA genotypes: 19
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
  1 (4.2%)  6 (25.0%)  3 (12.5%) 14 (58.3%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.000e+00 1.000e-08 3.503e-04 7.608e-03 4.485e-03 7.769e-02 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            24          16            39 0.6666667    0.8125              0
  n.call call.rate
1     24         1


Build a HIBAG model with 10 individual classifiers:
    MAF threshold: NaN
    excluding 4 monomorphic SNPs
    # of SNPs randomly sampled as candidates for each selection: 19
    # of SNPs: 345
    # of samples: 31
    # of unique HLA alleles: 7
CPU flags: 64-bit, AVX512BW
# of threads: 1
[-] 2024-03-28 01:42:16
=== building individual classifier 1, out-of-bag (11/35.5%) ===
[1] 2024-03-28 01:42:16, oob acc: 95.45%, # of SNPs: 11, # of haplo: 22
=== building individual classifier 2, out-of-bag (11/35.5%) ===
[2] 2024-03-28 01:42:16, oob acc: 100.00%, # of SNPs: 13, # of haplo: 22
=== building individual classifier 3, out-of-bag (15/48.4%) ===
[3] 2024-03-28 01:42:16, oob acc: 83.33%, # of SNPs: 15, # of haplo: 23
=== building individual classifier 4, out-of-bag (14/45.2%) ===
[4] 2024-03-28 01:42:16, oob acc: 82.14%, # of SNPs: 8, # of haplo: 14
=== building individual classifier 5, out-of-bag (13/41.9%) ===
[5] 2024-03-28 01:42:16, oob acc: 88.46%, # of SNPs: 11, # of haplo: 34
=== building individual classifier 6, out-of-bag (10/32.3%) ===
[6] 2024-03-28 01:42:16, oob acc: 90.00%, # of SNPs: 11, # of haplo: 21
=== building individual classifier 7, out-of-bag (13/41.9%) ===
[7] 2024-03-28 01:42:16, oob acc: 92.31%, # of SNPs: 14, # of haplo: 23
=== building individual classifier 8, out-of-bag (13/41.9%) ===
[8] 2024-03-28 01:42:16, oob acc: 96.15%, # of SNPs: 11, # of haplo: 16
=== building individual classifier 9, out-of-bag (14/45.2%) ===
[9] 2024-03-28 01:42:16, oob acc: 89.29%, # of SNPs: 12, # of haplo: 19
=== building individual classifier 10, out-of-bag (11/35.5%) ===
[10] 2024-03-28 01:42:16, oob acc: 86.36%, # of SNPs: 8, # of haplo: 13
Calculating matching proportion:
       Min.    0.1% Qu.      1% Qu.     1st Qu.      Median     3rd Qu. 
0.001972961 0.001998819 0.002231547 0.005363515 0.008831104 0.018431530 
       Max.        Mean          SD 
0.537093886 0.028877632 0.094687228 
Accuracy with training data: 96.77%
Out-of-bag accuracy: 90.35%
Gene: HLA-DQA1
Training dataset: 31 samples X 345 SNPs
    # of HLA alleles: 7
    # of individual classifiers: 10
    total # of SNPs used: 80
    avg. # of SNPs in an individual classifier: 11.40
        (sd: 2.27, min: 8, max: 15, median: 11.00)
    avg. # of haplotypes in an individual classifier: 20.70
        (sd: 5.96, min: 13, max: 34, median: 21.50)
    avg. out-of-bag accuracy: 90.35%
        (sd: 5.72%, min: 82.14%, max: 100.00%, median: 89.64%)
Matching proportion:
       Min.    0.1% Qu.      1% Qu.     1st Qu.      Median     3rd Qu. 
0.001972961 0.001998819 0.002231547 0.005363515 0.008831104 0.018431530 
       Max.        Mean          SD 
0.537093886 0.028877632 0.094687228 
Genome assembly: hg19
HIBAG model for HLA-DQA1:
    10 individual classifiers
    345 SNPs
    7 unique HLA alleles: 01:01, 01:02, 01:03, ...
Prediction:
    based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
    match.type="--"   missing SNPs #                
           Position         0 (0.0%) *being used [1]
         Pos+Allele         0 (0.0%)             [2]
    RefSNP+Position         0 (0.0%)                
             RefSNP         0 (0.0%)                
      [1]: useful if ambiguous strands on array-based platforms
      [2]: suggested if the model and test data have been matched to the same reference genome
    Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 29
CPU flags: 64-bit, AVX512BW
# of threads: 1
Predicting (2024-03-28 01:42:16)	0%
Predicting (2024-03-28 01:42:16)	100%
Gene: HLA-DQA1
Range: [32605169bp, 32612152bp] on hg19
# of samples: 29
# of unique HLA alleles: 6
# of unique HLA genotypes: 14
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
 5 (17.2%)  5 (17.2%)   2 (6.9%) 17 (58.6%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000001 0.0019253 0.0069908 0.0532601 0.0167536 0.5404845 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            29          21            49 0.7241379 0.8448276              0
  n.call call.rate
1     29         1


Build a HIBAG model with 10 individual classifiers:
    MAF threshold: NaN
    excluding 6 monomorphic SNPs
    # of SNPs randomly sampled as candidates for each selection: 19
    # of SNPs: 350
    # of samples: 34
    # of unique HLA alleles: 12
CPU flags: 64-bit, AVX512BW
# of threads: 1
[-] 2024-03-28 01:42:17
=== building individual classifier 1, out-of-bag (11/32.4%) ===
[1] 2024-03-28 01:42:17, oob acc: 86.36%, # of SNPs: 13, # of haplo: 34
=== building individual classifier 2, out-of-bag (13/38.2%) ===
[2] 2024-03-28 01:42:17, oob acc: 76.92%, # of SNPs: 21, # of haplo: 42
=== building individual classifier 3, out-of-bag (13/38.2%) ===
[3] 2024-03-28 01:42:17, oob acc: 80.77%, # of SNPs: 10, # of haplo: 17
=== building individual classifier 4, out-of-bag (13/38.2%) ===
[4] 2024-03-28 01:42:17, oob acc: 92.31%, # of SNPs: 22, # of haplo: 78
=== building individual classifier 5, out-of-bag (13/38.2%) ===
[5] 2024-03-28 01:42:17, oob acc: 92.31%, # of SNPs: 11, # of haplo: 40
=== building individual classifier 6, out-of-bag (14/41.2%) ===
[6] 2024-03-28 01:42:17, oob acc: 71.43%, # of SNPs: 8, # of haplo: 22
=== building individual classifier 7, out-of-bag (14/41.2%) ===
[7] 2024-03-28 01:42:17, oob acc: 71.43%, # of SNPs: 14, # of haplo: 53
=== building individual classifier 8, out-of-bag (11/32.4%) ===
[8] 2024-03-28 01:42:17, oob acc: 86.36%, # of SNPs: 14, # of haplo: 40
=== building individual classifier 9, out-of-bag (14/41.2%) ===
[9] 2024-03-28 01:42:17, oob acc: 100.00%, # of SNPs: 16, # of haplo: 56
=== building individual classifier 10, out-of-bag (13/38.2%) ===
[10] 2024-03-28 01:42:17, oob acc: 88.46%, # of SNPs: 14, # of haplo: 34
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0003282346 0.0003687353 0.0007332412 0.0038570393 0.0073528147 0.0148594626 
        Max.         Mean           SD 
0.3073781820 0.0225078064 0.0573939534 
Accuracy with training data: 98.53%
Out-of-bag accuracy: 84.64%
Gene: HLA-DQB1
Training dataset: 34 samples X 350 SNPs
    # of HLA alleles: 12
    # of individual classifiers: 10
    total # of SNPs used: 99
    avg. # of SNPs in an individual classifier: 14.30
        (sd: 4.45, min: 8, max: 22, median: 14.00)
    avg. # of haplotypes in an individual classifier: 41.60
        (sd: 17.55, min: 17, max: 78, median: 40.00)
    avg. out-of-bag accuracy: 84.64%
        (sd: 9.41%, min: 71.43%, max: 100.00%, median: 86.36%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0003282346 0.0003687353 0.0007332412 0.0038570393 0.0073528147 0.0148594626 
        Max.         Mean           SD 
0.3073781820 0.0225078064 0.0573939534 
Genome assembly: hg19
HIBAG model for HLA-DQB1:
    10 individual classifiers
    350 SNPs
    12 unique HLA alleles: 02:01, 02:02, 03:01, ...
Prediction:
    based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
    match.type="--"   missing SNPs #                
           Position         0 (0.0%) *being used [1]
         Pos+Allele         0 (0.0%)             [2]
    RefSNP+Position         0 (0.0%)                
             RefSNP         0 (0.0%)                
      [1]: useful if ambiguous strands on array-based platforms
      [2]: suggested if the model and test data have been matched to the same reference genome
    Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 26
CPU flags: 64-bit, AVX512BW
# of threads: 1
Predicting (2024-03-28 01:42:17)	0%
Predicting (2024-03-28 01:42:17)	100%
Gene: HLA-DQB1
Range: [32627241bp, 32634466bp] on hg19
# of samples: 26
# of unique HLA alleles: 10
# of unique HLA genotypes: 17
Posterior probability:
  [0,0.25) [0.25,0.5) [0.5,0.75)   [0.75,1] 
 3 (11.5%)  7 (26.9%)  5 (19.2%) 11 (42.3%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0002253 0.0018486 0.0308488 0.0099906 0.4023552 
  total.num.ind crt.num.ind crt.num.haplo   acc.ind acc.haplo call.threshold
1            26          21            46 0.8076923 0.8846154              0
  n.call call.rate
1     26         1


Build a HIBAG model with 10 individual classifiers:
    MAF threshold: NaN
    excluding 5 monomorphic SNPs
    # of SNPs randomly sampled as candidates for each selection: 18
    # of SNPs: 322
    # of samples: 35
    # of unique HLA alleles: 20
CPU flags: 64-bit, AVX512BW
# of threads: 1
[-] 2024-03-28 01:42:17
=== building individual classifier 1, out-of-bag (15/42.9%) ===
[1] 2024-03-28 01:42:17, oob acc: 70.00%, # of SNPs: 17, # of haplo: 77
=== building individual classifier 2, out-of-bag (16/45.7%) ===
[2] 2024-03-28 01:42:18, oob acc: 56.25%, # of SNPs: 19, # of haplo: 92
=== building individual classifier 3, out-of-bag (15/42.9%) ===
[3] 2024-03-28 01:42:18, oob acc: 70.00%, # of SNPs: 11, # of haplo: 32
=== building individual classifier 4, out-of-bag (15/42.9%) ===
[4] 2024-03-28 01:42:18, oob acc: 73.33%, # of SNPs: 20, # of haplo: 138
=== building individual classifier 5, out-of-bag (14/40.0%) ===
[5] 2024-03-28 01:42:18, oob acc: 75.00%, # of SNPs: 17, # of haplo: 73
=== building individual classifier 6, out-of-bag (12/34.3%) ===
[6] 2024-03-28 01:42:18, oob acc: 66.67%, # of SNPs: 20, # of haplo: 154
=== building individual classifier 7, out-of-bag (11/31.4%) ===
[7] 2024-03-28 01:42:19, oob acc: 63.64%, # of SNPs: 15, # of haplo: 38
=== building individual classifier 8, out-of-bag (11/31.4%) ===
[8] 2024-03-28 01:42:19, oob acc: 68.18%, # of SNPs: 19, # of haplo: 115
=== building individual classifier 9, out-of-bag (12/34.3%) ===
[9] 2024-03-28 01:42:19, oob acc: 83.33%, # of SNPs: 21, # of haplo: 141
=== building individual classifier 10, out-of-bag (11/31.4%) ===
[10] 2024-03-28 01:42:19, oob acc: 81.82%, # of SNPs: 15, # of haplo: 89
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
5.613590e-05 7.650057e-05 2.597826e-04 1.561236e-03 3.938807e-03 8.196339e-03 
        Max.         Mean           SD 
4.747113e-01 4.329295e-02 1.276469e-01 
Accuracy with training data: 92.86%
Out-of-bag accuracy: 70.82%
Gene: HLA-DRB1
Training dataset: 35 samples X 322 SNPs
    # of HLA alleles: 20
    # of individual classifiers: 10
    total # of SNPs used: 119
    avg. # of SNPs in an individual classifier: 17.40
        (sd: 3.06, min: 11, max: 21, median: 18.00)
    avg. # of haplotypes in an individual classifier: 94.90
        (sd: 42.05, min: 32, max: 154, median: 90.50)
    avg. out-of-bag accuracy: 70.82%
        (sd: 8.10%, min: 56.25%, max: 83.33%, median: 70.00%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
5.613590e-05 7.650057e-05 2.597826e-04 1.561236e-03 3.938807e-03 8.196339e-03 
        Max.         Mean           SD 
4.747113e-01 4.329295e-02 1.276469e-01 
Genome assembly: hg19
HIBAG model for HLA-DRB1:
    10 individual classifiers
    322 SNPs
    20 unique HLA alleles: 01:01, 01:03, 03:01, ...
Prediction:
    based on the averaged posterior probabilities
Model assembly: hg19, SNP assembly: hg19
Matching the SNPs between the model and the test data:
    match.type="--"   missing SNPs #                
           Position         0 (0.0%) *being used [1]
         Pos+Allele         0 (0.0%)             [2]
    RefSNP+Position         0 (0.0%)                
             RefSNP         0 (0.0%)                
      [1]: useful if ambiguous strands on array-based platforms
      [2]: suggested if the model and test data have been matched to the same reference genome
    Model platform: not applicable
No allelic strand or A/B allele is flipped.
# of samples: 25
CPU flags: 64-bit, AVX512BW
# of threads: 1
Predicting (2024-03-28 01:42:19)	0%
Predicting (2024-03-28 01:42:19)	100%
Gene: HLA-DRB1
Range: [32546546bp, 32557613bp] on hg19
# of samples: 25
# 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] 
 5 (20.0%)  3 (12.0%) 11 (44.0%)  6 (24.0%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
3.062e-05 3.538e-04 2.041e-03 7.954e-03 3.179e-03 1.275e-01 
  total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1            25          16            40    0.64       0.8              0
  n.call call.rate
1     25         1


> 
> 
> proc.time()
   user  system elapsed 
   5.45    0.17    5.65 

Example timings

HIBAG.Rcheck/HIBAG-Ex.timings

nameusersystemelapsed
HIBAG-package0.360.030.39
hlaAllele0.000.000.02
hlaAlleleDigit0.020.000.02
hlaAlleleSubset000
hlaAlleleToVCF1.680.001.68
hlaAssocTest0.720.020.75
hlaAttrBagging0.360.010.41
hlaBED2Geno0.080.000.08
hlaCheckAllele000
hlaCheckSNPs0.050.000.06
hlaCombineAllele0.000.010.01
hlaCombineModelObj0.220.000.22
hlaCompareAllele0.230.030.27
hlaConvSequence2.750.222.97
hlaDistance1.110.051.15
hlaFlankingSNP0.020.000.01
hlaGDS2Geno0.090.000.10
hlaGeno2PED0.050.000.08
hlaGenoAFreq000
hlaGenoCombine0.010.030.04
hlaGenoLD0.410.000.41
hlaGenoMFreq000
hlaGenoMRate000
hlaGenoMRate_Samp0.020.000.02
hlaGenoSubset000
hlaGenoSwitchStrand0.050.000.05
hlaLDMatrix1.360.121.50
hlaLociInfo000
hlaMakeSNPGeno0.010.000.01
hlaModelFiles0.190.000.19
hlaModelFromObj0.060.000.08
hlaOutOfBag0.520.000.51
hlaParallelAttrBagging0.580.061.22
hlaPredMerge0.260.000.27
hlaPredict0.300.000.29
hlaPublish0.280.020.30
hlaReport0.230.020.25
hlaReportPlot1.770.001.78
hlaSNPID000
hlaSampleAllele0.010.000.02
hlaSetKernelTarget000
hlaSplitAllele0.050.000.05
hlaSubModelObj0.050.000.06
hlaUniqueAllele000
plot.hlaAttrBagObj0.300.010.32
print.hlaAttrBagClass0.130.000.12
summary.hlaSNPGenoClass000