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##############################################################################
###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:HIBAG.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings HIBAG_1.38.2.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/Users/biocbuild/bbs-3.18-bioc-mac-arm64/meat/HIBAG.Rcheck’
* using R version 4.3.3 (2024-02-29)
* using platform: aarch64-apple-darwin20 (64-bit)
* R was compiled by
    Apple clang version 14.0.0 (clang-1400.0.29.202)
    GNU Fortran (GCC) 12.2.0
* running under: macOS Ventura 13.6.1
* 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 for sufficient/correct file permissions ... OK
* checking whether package ‘HIBAG’ can be installed ... OK
* used C compiler: ‘Apple clang version 15.0.0 (clang-1500.0.40.1)’
* used C++ compiler: ‘Apple clang version 15.0.0 (clang-1500.0.40.1)’
* used SDK: ‘MacOSX11.3.sdk’
* 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 is not available
File ‘HIBAG/libs/HIBAG.so’:
  Found non-API call to R: ‘R_new_custom_connection’

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
  ‘/Users/biocbuild/bbs-3.18-bioc-mac-arm64/meat/HIBAG.Rcheck/00check.log’
for details.

##############################################################################
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###
### Running command:
###
###   /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL HIBAG
###
##############################################################################
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* installing to library ‘/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library’
* installing *source* package ‘HIBAG’ ...
** using staged installation
** libs
using C compiler: ‘Apple clang version 15.0.0 (clang-1500.0.40.1)’
using C++ compiler: ‘Apple clang version 15.0.0 (clang-1500.0.40.1)’
using C++11
using SDK: ‘MacOSX11.3.sdk’
clang++ -arch arm64 -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I../inst/include -I'/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/RcppParallel/include' -I/opt/R/arm64/include    -fPIC  -falign-functions=64 -Wall -g -O2  -c HIBAG.cpp -o HIBAG.o
clang++ -arch arm64 -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I../inst/include -I'/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/RcppParallel/include' -I/opt/R/arm64/include    -fPIC  -falign-functions=64 -Wall -g -O2  -c LibHLA.cpp -o LibHLA.o
clang++ -arch arm64 -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I../inst/include -I'/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/RcppParallel/include' -I/opt/R/arm64/include    -fPIC  -falign-functions=64 -Wall -g -O2  -c LibHLA_ext_avx.cpp -o LibHLA_ext_avx.o
clang++ -arch arm64 -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I../inst/include -I'/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/RcppParallel/include' -I/opt/R/arm64/include    -fPIC  -falign-functions=64 -Wall -g -O2  -c LibHLA_ext_avx2.cpp -o LibHLA_ext_avx2.o
clang++ -arch arm64 -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I../inst/include -I'/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/RcppParallel/include' -I/opt/R/arm64/include    -fPIC  -falign-functions=64 -Wall -g -O2  -c LibHLA_ext_avx512bw.cpp -o LibHLA_ext_avx512bw.o
clang++ -arch arm64 -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I../inst/include -I'/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/RcppParallel/include' -I/opt/R/arm64/include    -fPIC  -falign-functions=64 -Wall -g -O2  -c LibHLA_ext_avx512f.cpp -o LibHLA_ext_avx512f.o
clang++ -arch arm64 -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I../inst/include -I'/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/RcppParallel/include' -I/opt/R/arm64/include    -fPIC  -falign-functions=64 -Wall -g -O2  -c LibHLA_ext_avx512vpopcnt.cpp -o LibHLA_ext_avx512vpopcnt.o
clang++ -arch arm64 -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I../inst/include -I'/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/RcppParallel/include' -I/opt/R/arm64/include    -fPIC  -falign-functions=64 -Wall -g -O2  -c LibHLA_ext_sse2.cpp -o LibHLA_ext_sse2.o
clang++ -arch arm64 -std=gnu++11 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I../inst/include -I'/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/RcppParallel/include' -I/opt/R/arm64/include    -fPIC  -falign-functions=64 -Wall -g -O2  -c LibHLA_ext_sse4_2.cpp -o LibHLA_ext_sse4_2.o
clang -arch arm64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I../inst/include -I'/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/RcppParallel/include' -I/opt/R/arm64/include    -fPIC  -falign-functions=64 -Wall -g -O2  -c samtools_ext.c -o samtools_ext.o
clang++ -arch arm64 -std=gnu++11 -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -L/Library/Frameworks/R.framework/Resources/lib -L/opt/R/arm64/lib -o HIBAG.so 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 -F/Library/Frameworks/R.framework/.. -framework R -Wl,-framework -Wl,CoreFoundation
installing to /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/00LOCK-HIBAG/00new/HIBAG/libs
** 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
** checking absolute paths in shared objects and dynamic libraries
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (HIBAG)

R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20 (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)
> 
> 
> #############################################################
> 
> # 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
# of threads: 1
[-] 2024-04-17 16:15:02
=== building individual classifier 1, out-of-bag (11/32.4%) ===
[1] 2024-04-17 16:15:02, oob acc: 77.27%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 2, out-of-bag (13/38.2%) ===
[2] 2024-04-17 16:15:02, oob acc: 88.46%, # of SNPs: 11, # of haplo: 48
=== building individual classifier 3, out-of-bag (14/41.2%) ===
[3] 2024-04-17 16:15:03, oob acc: 85.71%, # of SNPs: 11, # of haplo: 14
=== building individual classifier 4, out-of-bag (10/29.4%) ===
[4] 2024-04-17 16:15:03, oob acc: 75.00%, # of SNPs: 13, # of haplo: 23
=== building individual classifier 5, out-of-bag (17/50.0%) ===
[5] 2024-04-17 16:15:03, oob acc: 79.41%, # of SNPs: 13, # of haplo: 34
=== building individual classifier 6, out-of-bag (11/32.4%) ===
[6] 2024-04-17 16:15:03, oob acc: 100.00%, # of SNPs: 19, # of haplo: 72
=== building individual classifier 7, out-of-bag (9/26.5%) ===
[7] 2024-04-17 16:15:03, oob acc: 100.00%, # of SNPs: 17, # of haplo: 37
=== building individual classifier 8, out-of-bag (13/38.2%) ===
[8] 2024-04-17 16:15:03, oob acc: 84.62%, # of SNPs: 14, # of haplo: 56
=== building individual classifier 9, out-of-bag (14/41.2%) ===
[9] 2024-04-17 16:15:03, oob acc: 89.29%, # of SNPs: 13, # of haplo: 34
=== building individual classifier 10, out-of-bag (13/38.2%) ===
[10] 2024-04-17 16:15:03, oob acc: 80.77%, # of SNPs: 14, # of haplo: 24
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004220405 0.0004263127 0.0004647623 0.0055051726 0.0109090365 0.0235725089 
        Max.         Mean           SD 
0.4696060704 0.0448179210 0.1063804198 
Accuracy with training data: 98.53%
Out-of-bag accuracy: 86.05%
Gene: HLA-A
Training dataset: 34 samples X 264 SNPs
    # of HLA alleles: 14
    # of individual classifiers: 10
    total # of SNPs used: 92
    avg. # of SNPs in an individual classifier: 13.80
        (sd: 2.49, min: 11, max: 19, median: 13.00)
    avg. # of haplotypes in an individual classifier: 36.50
        (sd: 17.68, min: 14, max: 72, median: 34.00)
    avg. out-of-bag accuracy: 86.05%
        (sd: 8.68%, min: 75.00%, max: 100.00%, median: 85.16%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0004220405 0.0004263127 0.0004647623 0.0055051726 0.0109090365 0.0235725089 
        Max.         Mean           SD 
0.4696060704 0.0448179210 0.1063804198 
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
# of threads: 1
Predicting (2024-04-17 16:15:03)	0%
Predicting (2024-04-17 16:15:03)	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 0.002746 0.007371 0.031621 0.024572 0.469606 
  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
# of threads: 1
[-] 2024-04-17 16:15:03
=== building individual classifier 1, out-of-bag (12/42.9%) ===
[1] 2024-04-17 16:15:03, oob acc: 58.33%, # of SNPs: 17, # of haplo: 52
=== building individual classifier 2, out-of-bag (11/39.3%) ===
[2] 2024-04-17 16:15:04, oob acc: 63.64%, # of SNPs: 18, # of haplo: 51
=== building individual classifier 3, out-of-bag (13/46.4%) ===
[3] 2024-04-17 16:15:04, oob acc: 50.00%, # of SNPs: 15, # of haplo: 29
=== building individual classifier 4, out-of-bag (11/39.3%) ===
[4] 2024-04-17 16:15:04, oob acc: 59.09%, # of SNPs: 12, # of haplo: 57
=== building individual classifier 5, out-of-bag (11/39.3%) ===
[5] 2024-04-17 16:15:04, oob acc: 63.64%, # of SNPs: 15, # of haplo: 86
=== building individual classifier 6, out-of-bag (12/42.9%) ===
[6] 2024-04-17 16:15:04, oob acc: 79.17%, # of SNPs: 18, # of haplo: 66
=== building individual classifier 7, out-of-bag (12/42.9%) ===
[7] 2024-04-17 16:15:04, oob acc: 70.83%, # of SNPs: 15, # of haplo: 86
=== building individual classifier 8, out-of-bag (9/32.1%) ===
[8] 2024-04-17 16:15:05, oob acc: 77.78%, # of SNPs: 16, # of haplo: 117
=== building individual classifier 9, out-of-bag (9/32.1%) ===
[9] 2024-04-17 16:15:05, oob acc: 77.78%, # of SNPs: 18, # of haplo: 92
=== building individual classifier 10, out-of-bag (9/32.1%) ===
[10] 2024-04-17 16:15:05, oob acc: 66.67%, # of SNPs: 16, # of haplo: 91
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.972868e-05 8.070947e-05 8.953662e-05 2.940413e-03 7.764710e-03 1.211147e-02 
        Max.         Mean           SD 
1.254185e-01 1.298944e-02 2.365315e-02 
Accuracy with training data: 100.00%
Out-of-bag accuracy: 66.69%
Gene: HLA-B
Training dataset: 28 samples X 340 SNPs
    # of HLA alleles: 22
    # of individual classifiers: 10
    total # of SNPs used: 119
    avg. # of SNPs in an individual classifier: 16.00
        (sd: 1.89, min: 12, max: 18, median: 16.00)
    avg. # of haplotypes in an individual classifier: 72.70
        (sd: 26.09, min: 29, max: 117, median: 76.00)
    avg. out-of-bag accuracy: 66.69%
        (sd: 9.68%, min: 50.00%, max: 79.17%, median: 65.15%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.972868e-05 8.070947e-05 8.953662e-05 2.940413e-03 7.764710e-03 1.211147e-02 
        Max.         Mean           SD 
1.254185e-01 1.298944e-02 2.365315e-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
# of threads: 1
Predicting (2024-04-17 16:15:05)	0%
Predicting (2024-04-17 16:15:05)	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 1.840e-02 5.152e-03 1.367e-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
# of threads: 1
[-] 2024-04-17 16:15:05
=== building individual classifier 1, out-of-bag (13/36.1%) ===
[1] 2024-04-17 16:15:05, oob acc: 80.77%, # of SNPs: 19, # of haplo: 40
=== building individual classifier 2, out-of-bag (11/30.6%) ===
[2] 2024-04-17 16:15:06, oob acc: 90.91%, # of SNPs: 32, # of haplo: 32
=== building individual classifier 3, out-of-bag (14/38.9%) ===
[3] 2024-04-17 16:15:06, oob acc: 89.29%, # of SNPs: 19, # of haplo: 43
=== building individual classifier 4, out-of-bag (13/36.1%) ===
[4] 2024-04-17 16:15:06, oob acc: 84.62%, # of SNPs: 19, # of haplo: 72
=== building individual classifier 5, out-of-bag (10/27.8%) ===
[5] 2024-04-17 16:15:06, oob acc: 90.00%, # of SNPs: 19, # of haplo: 66
=== building individual classifier 6, out-of-bag (10/27.8%) ===
[6] 2024-04-17 16:15:07, oob acc: 95.00%, # of SNPs: 21, # of haplo: 59
=== building individual classifier 7, out-of-bag (16/44.4%) ===
[7] 2024-04-17 16:15:07, oob acc: 90.62%, # of SNPs: 18, # of haplo: 25
=== building individual classifier 8, out-of-bag (14/38.9%) ===
[8] 2024-04-17 16:15:07, oob acc: 89.29%, # of SNPs: 23, # of haplo: 57
=== building individual classifier 9, out-of-bag (13/36.1%) ===
[9] 2024-04-17 16:15:07, oob acc: 84.62%, # of SNPs: 18, # of haplo: 39
=== building individual classifier 10, out-of-bag (14/38.9%) ===
[10] 2024-04-17 16:15:07, oob acc: 89.29%, # of SNPs: 35, # of haplo: 62
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0007653736 0.0007682927 0.0007945643 0.0017428621 0.0044732464 0.0093175730 
        Max.         Mean           SD 
0.0703539734 0.0088728477 0.0132051834 
Accuracy with training data: 100.00%
Out-of-bag accuracy: 88.44%
Gene: HLA-C
Training dataset: 36 samples X 354 SNPs
    # of HLA alleles: 17
    # of individual classifiers: 10
    total # of SNPs used: 135
    avg. # of SNPs in an individual classifier: 22.30
        (sd: 6.13, min: 18, max: 35, median: 19.00)
    avg. # of haplotypes in an individual classifier: 49.50
        (sd: 15.74, min: 25, max: 72, median: 50.00)
    avg. out-of-bag accuracy: 88.44%
        (sd: 4.04%, min: 80.77%, max: 95.00%, median: 89.29%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
0.0007653736 0.0007682927 0.0007945643 0.0017428621 0.0044732464 0.0093175730 
        Max.         Mean           SD 
0.0703539734 0.0088728477 0.0132051834 
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
# of threads: 1
Predicting (2024-04-17 16:15:07)	0%
Predicting (2024-04-17 16:15:07)	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] 
  2 (8.3%)  3 (12.5%)  6 (25.0%) 13 (54.2%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0000000 0.0002058 0.0058893 0.0035911 0.0468290 
  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
# of threads: 1
[-] 2024-04-17 16:15:07
=== building individual classifier 1, out-of-bag (11/35.5%) ===
[1] 2024-04-17 16:15:07, oob acc: 95.45%, # of SNPs: 11, # of haplo: 22
=== building individual classifier 2, out-of-bag (11/35.5%) ===
[2] 2024-04-17 16:15:07, oob acc: 100.00%, # of SNPs: 13, # of haplo: 22
=== building individual classifier 3, out-of-bag (15/48.4%) ===
[3] 2024-04-17 16:15:07, oob acc: 83.33%, # of SNPs: 15, # of haplo: 23
=== building individual classifier 4, out-of-bag (14/45.2%) ===
[4] 2024-04-17 16:15:07, oob acc: 82.14%, # of SNPs: 8, # of haplo: 14
=== building individual classifier 5, out-of-bag (13/41.9%) ===
[5] 2024-04-17 16:15:08, oob acc: 88.46%, # of SNPs: 11, # of haplo: 34
=== building individual classifier 6, out-of-bag (10/32.3%) ===
[6] 2024-04-17 16:15:08, oob acc: 90.00%, # of SNPs: 11, # of haplo: 21
=== building individual classifier 7, out-of-bag (13/41.9%) ===
[7] 2024-04-17 16:15:08, oob acc: 92.31%, # of SNPs: 14, # of haplo: 23
=== building individual classifier 8, out-of-bag (13/41.9%) ===
[8] 2024-04-17 16:15:08, oob acc: 96.15%, # of SNPs: 11, # of haplo: 16
=== building individual classifier 9, out-of-bag (14/45.2%) ===
[9] 2024-04-17 16:15:08, oob acc: 89.29%, # of SNPs: 12, # of haplo: 19
=== building individual classifier 10, out-of-bag (11/35.5%) ===
[10] 2024-04-17 16:15:08, 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
# of threads: 1
Predicting (2024-04-17 16:15:08)	0%
Predicting (2024-04-17 16:15:08)	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
# of threads: 1
[-] 2024-04-17 16:15:08
=== building individual classifier 1, out-of-bag (11/32.4%) ===
[1] 2024-04-17 16:15:08, oob acc: 86.36%, # of SNPs: 13, # of haplo: 34
=== building individual classifier 2, out-of-bag (13/38.2%) ===
[2] 2024-04-17 16:15:08, oob acc: 76.92%, # of SNPs: 21, # of haplo: 42
=== building individual classifier 3, out-of-bag (13/38.2%) ===
[3] 2024-04-17 16:15:08, oob acc: 80.77%, # of SNPs: 10, # of haplo: 17
=== building individual classifier 4, out-of-bag (13/38.2%) ===
[4] 2024-04-17 16:15:08, oob acc: 92.31%, # of SNPs: 22, # of haplo: 78
=== building individual classifier 5, out-of-bag (13/38.2%) ===
[5] 2024-04-17 16:15:08, oob acc: 92.31%, # of SNPs: 11, # of haplo: 40
=== building individual classifier 6, out-of-bag (14/41.2%) ===
[6] 2024-04-17 16:15:08, oob acc: 71.43%, # of SNPs: 8, # of haplo: 22
=== building individual classifier 7, out-of-bag (14/41.2%) ===
[7] 2024-04-17 16:15:09, oob acc: 71.43%, # of SNPs: 14, # of haplo: 53
=== building individual classifier 8, out-of-bag (11/32.4%) ===
[8] 2024-04-17 16:15:09, oob acc: 86.36%, # of SNPs: 14, # of haplo: 40
=== building individual classifier 9, out-of-bag (14/41.2%) ===
[9] 2024-04-17 16:15:09, oob acc: 100.00%, # of SNPs: 16, # of haplo: 56
=== building individual classifier 10, out-of-bag (13/38.2%) ===
[10] 2024-04-17 16:15:09, 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
# of threads: 1
Predicting (2024-04-17 16:15:09)	0%
Predicting (2024-04-17 16:15:09)	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
# of threads: 1
[-] 2024-04-17 16:15:09
=== building individual classifier 1, out-of-bag (15/42.9%) ===
[1] 2024-04-17 16:15:10, oob acc: 66.67%, # of SNPs: 21, # of haplo: 132
=== building individual classifier 2, out-of-bag (13/37.1%) ===
[2] 2024-04-17 16:15:10, oob acc: 61.54%, # of SNPs: 19, # of haplo: 91
=== building individual classifier 3, out-of-bag (15/42.9%) ===
[3] 2024-04-17 16:15:10, oob acc: 70.00%, # of SNPs: 10, # of haplo: 33
=== building individual classifier 4, out-of-bag (13/37.1%) ===
[4] 2024-04-17 16:15:11, oob acc: 73.08%, # of SNPs: 17, # of haplo: 121
=== building individual classifier 5, out-of-bag (12/34.3%) ===
[5] 2024-04-17 16:15:12, oob acc: 79.17%, # of SNPs: 17, # of haplo: 153
=== building individual classifier 6, out-of-bag (12/34.3%) ===
[6] 2024-04-17 16:15:13, oob acc: 66.67%, # of SNPs: 20, # of haplo: 154
=== building individual classifier 7, out-of-bag (11/31.4%) ===
[7] 2024-04-17 16:15:13, oob acc: 63.64%, # of SNPs: 15, # of haplo: 38
=== building individual classifier 8, out-of-bag (11/31.4%) ===
[8] 2024-04-17 16:15:14, oob acc: 68.18%, # of SNPs: 19, # of haplo: 115
=== building individual classifier 9, out-of-bag (12/34.3%) ===
[9] 2024-04-17 16:15:14, oob acc: 79.17%, # of SNPs: 23, # of haplo: 126
=== building individual classifier 10, out-of-bag (11/31.4%) ===
[10] 2024-04-17 16:15:15, oob acc: 81.82%, # of SNPs: 22, # of haplo: 146
Calculating matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.654289e-05 1.001066e-04 3.121801e-04 1.848734e-03 2.693889e-03 5.721191e-03 
        Max.         Mean           SD 
3.820492e-01 3.558536e-02 1.056328e-01 
Accuracy with training data: 94.29%
Out-of-bag accuracy: 70.99%
Gene: HLA-DRB1
Training dataset: 35 samples X 322 SNPs
    # of HLA alleles: 20
    # of individual classifiers: 10
    total # of SNPs used: 116
    avg. # of SNPs in an individual classifier: 18.30
        (sd: 3.80, min: 10, max: 23, median: 19.00)
    avg. # of haplotypes in an individual classifier: 110.90
        (sd: 44.01, min: 33, max: 154, median: 123.50)
    avg. out-of-bag accuracy: 70.99%
        (sd: 7.03%, min: 61.54%, max: 81.82%, median: 69.09%)
Matching proportion:
        Min.     0.1% Qu.       1% Qu.      1st Qu.       Median      3rd Qu. 
7.654289e-05 1.001066e-04 3.121801e-04 1.848734e-03 2.693889e-03 5.721191e-03 
        Max.         Mean           SD 
3.820492e-01 3.558536e-02 1.056328e-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
# of threads: 1
Predicting (2024-04-17 16:15:15)	0%
Predicting (2024-04-17 16:15:15)	100%
Gene: HLA-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%)  6 (24.0%)  6 (24.0%)  9 (36.0%) 
Matching proportion of SNP haplotype:
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
0.0000000 0.0003674 0.0015170 0.0053025 0.0024490 0.0880370 
  total.num.ind crt.num.ind crt.num.haplo acc.ind acc.haplo call.threshold
1            25          15            39     0.6      0.78              0
  n.call call.rate
1     25         1


> 
> 
> proc.time()
   user  system elapsed 
 13.381   0.111  13.567