Back to Multiple platform build/check report for BioC 3.20: simplified long |
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This page was generated on 2024-08-21 11:41 -0400 (Wed, 21 Aug 2024).
Hostname | OS | Arch (*) | R version | Installed pkgs |
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nebbiolo2 | Linux (Ubuntu 22.04.3 LTS) | x86_64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4703 |
palomino8 | Windows Server 2022 Datacenter | x64 | 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" | 4440 |
lconway | macOS 12.7.1 Monterey | x86_64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4472 |
kjohnson3 | macOS 13.6.5 Ventura | arm64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4421 |
kunpeng2 | Linux (openEuler 22.03 LTS-SP1) | aarch64 | 4.4.1 (2024-06-14) -- "Race for Your Life" | 4414 |
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 940/2255 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
HIBAG 1.41.1 (landing page) Xiuwen Zheng
| nebbiolo2 | Linux (Ubuntu 22.04.3 LTS) / x86_64 | OK | OK | OK | ![]() | ||||||||
palomino8 | Windows Server 2022 Datacenter / x64 | OK | OK | OK | OK | ![]() | ||||||||
lconway | macOS 12.7.1 Monterey / x86_64 | OK | OK | OK | OK | ![]() | ||||||||
kjohnson3 | macOS 13.6.5 Ventura / arm64 | OK | OK | OK | OK | ![]() | ||||||||
kunpeng2 | Linux (openEuler 22.03 LTS-SP1) / aarch64 | OK | OK | OK | ||||||||||
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. |
Package: HIBAG |
Version: 1.41.1 |
Command: F:\biocbuild\bbs-3.20-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:HIBAG.install-out.txt --library=F:\biocbuild\bbs-3.20-bioc\R\library --no-vignettes --timings HIBAG_1.41.1.tar.gz |
StartedAt: 2024-08-21 01:38:17 -0400 (Wed, 21 Aug 2024) |
EndedAt: 2024-08-21 01:39:58 -0400 (Wed, 21 Aug 2024) |
EllapsedTime: 101.4 seconds |
RetCode: 0 |
Status: OK |
CheckDir: HIBAG.Rcheck |
Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### F:\biocbuild\bbs-3.20-bioc\R\bin\R.exe CMD check --no-multiarch --install=check:HIBAG.install-out.txt --library=F:\biocbuild\bbs-3.20-bioc\R\library --no-vignettes --timings HIBAG_1.41.1.tar.gz ### ############################################################################## ############################################################################## * using log directory 'F:/biocbuild/bbs-3.20-bioc/meat/HIBAG.Rcheck' * using R version 4.4.1 (2024-06-14 ucrt) * using platform: x86_64-w64-mingw32 * R was compiled by gcc.exe (GCC) 13.2.0 GNU Fortran (GCC) 13.2.0 * 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 '1.41.1' * 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) 13.2.0' * used C++ compiler: 'G__~1.EXE (GCC) 13.2.0' * 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 code 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 whether 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-3.20-bioc/R/library/HIBAG/libs/x64/HIBAG.dll': Found '_exit', possibly from '_exit' (C) 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 ... 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-3.20-bioc/meat/HIBAG.Rcheck/00check.log' for details.
HIBAG.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### F:\biocbuild\bbs-3.20-bioc\R\bin\R.exe CMD INSTALL HIBAG ### ############################################################################## ############################################################################## * installing to library 'F:/biocbuild/bbs-3.20-bioc/R/library' * installing *source* package 'HIBAG' ... ** using staged installation ** libs using C compiler: 'gcc.exe (GCC) 13.2.0' using C++ compiler: 'G__~1.EXE (GCC) 13.2.0' using C++11 g++ -std=gnu++11 -I"F:/biocbuild/bbs-3.20-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.20-bioc/R/library/RcppParallel/include' -I"C:/rtools44/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-3.20-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.20-bioc/R/library/RcppParallel/include' -I"C:/rtools44/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-3.20-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.20-bioc/R/library/RcppParallel/include' -I"C:/rtools44/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-3.20-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.20-bioc/R/library/RcppParallel/include' -I"C:/rtools44/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-3.20-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.20-bioc/R/library/RcppParallel/include' -I"C:/rtools44/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-3.20-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.20-bioc/R/library/RcppParallel/include' -I"C:/rtools44/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-3.20-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.20-bioc/R/library/RcppParallel/include' -I"C:/rtools44/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-3.20-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.20-bioc/R/library/RcppParallel/include' -I"C:/rtools44/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-3.20-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.20-bioc/R/library/RcppParallel/include' -I"C:/rtools44/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-3.20-bioc/R/include" -DNDEBUG -I'F:/biocbuild/bbs-3.20-bioc/R/library/RcppParallel/include' -I"C:/rtools44/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-3.20-bioc/R/library/RcppParallel/lib/x64 -ltbb -ltbbmalloc -LC:/rtools44/x86_64-w64-mingw32.static.posix/lib/x64 -LC:/rtools44/x86_64-w64-mingw32.static.posix/lib -LF:/biocbuild/bbs-3.20-bioc/R/bin/x64 -lR installing to F:/biocbuild/bbs-3.20-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)
HIBAG.Rcheck/tests/runTests.Rout
R version 4.4.1 (2024-06-14 ucrt) -- "Race for Your Life" Copyright (C) 2024 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 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 [-] 2024-08-21 01:39:36 === building individual classifier 1, out-of-bag (11/32.4%) === [1] 2024-08-21 01:39:36, oob acc: 77.27%, # of SNPs: 13, # of haplo: 23 === building individual classifier 2, out-of-bag (13/38.2%) === [2] 2024-08-21 01:39:36, oob acc: 88.46%, # of SNPs: 12, # of haplo: 48 === building individual classifier 3, out-of-bag (14/41.2%) === [3] 2024-08-21 01:39:36, oob acc: 85.71%, # of SNPs: 11, # of haplo: 14 === building individual classifier 4, out-of-bag (10/29.4%) === [4] 2024-08-21 01:39:36, oob acc: 75.00%, # of SNPs: 13, # of haplo: 23 === building individual classifier 5, out-of-bag (17/50.0%) === [5] 2024-08-21 01:39:36, oob acc: 79.41%, # of SNPs: 13, # of haplo: 34 === building individual classifier 6, out-of-bag (11/32.4%) === [6] 2024-08-21 01:39:37, oob acc: 100.00%, # of SNPs: 19, # of haplo: 72 === building individual classifier 7, out-of-bag (9/26.5%) === [7] 2024-08-21 01:39:37, oob acc: 100.00%, # of SNPs: 17, # of haplo: 37 === building individual classifier 8, out-of-bag (13/38.2%) === [8] 2024-08-21 01:39:37, oob acc: 88.46%, # of SNPs: 15, # of haplo: 57 === building individual classifier 9, out-of-bag (15/44.1%) === [9] 2024-08-21 01:39:37, oob acc: 93.33%, # of SNPs: 13, # of haplo: 22 === building individual classifier 10, out-of-bag (13/38.2%) === [10] 2024-08-21 01:39:37, 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 (2024-08-21 01:39:37) 0% Predicting (2024-08-21 01:39:37) 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 [-] 2024-08-21 01:39:37 === building individual classifier 1, out-of-bag (12/42.9%) === [1] 2024-08-21 01:39:37, oob acc: 58.33%, # of SNPs: 17, # of haplo: 52 === building individual classifier 2, out-of-bag (11/39.3%) === [2] 2024-08-21 01:39:37, oob acc: 63.64%, # of SNPs: 18, # of haplo: 51 === building individual classifier 3, out-of-bag (13/46.4%) === [3] 2024-08-21 01:39:37, oob acc: 50.00%, # of SNPs: 15, # of haplo: 29 === building individual classifier 4, out-of-bag (11/39.3%) === [4] 2024-08-21 01:39:37, oob acc: 59.09%, # of SNPs: 12, # of haplo: 57 === building individual classifier 5, out-of-bag (11/39.3%) === [5] 2024-08-21 01:39:37, oob acc: 63.64%, # of SNPs: 15, # of haplo: 94 === building individual classifier 6, out-of-bag (12/42.9%) === [6] 2024-08-21 01:39:37, oob acc: 79.17%, # of SNPs: 18, # of haplo: 66 === building individual classifier 7, out-of-bag (12/42.9%) === [7] 2024-08-21 01:39:37, oob acc: 70.83%, # of SNPs: 15, # of haplo: 86 === building individual classifier 8, out-of-bag (9/32.1%) === [8] 2024-08-21 01:39:38, oob acc: 77.78%, # of SNPs: 16, # of haplo: 117 === building individual classifier 9, out-of-bag (9/32.1%) === [9] 2024-08-21 01:39:38, oob acc: 77.78%, # of SNPs: 18, # of haplo: 92 === building individual classifier 10, out-of-bag (9/32.1%) === [10] 2024-08-21 01:39:38, 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 (2024-08-21 01:39:38) 0% Predicting (2024-08-21 01:39:38) 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 [-] 2024-08-21 01:39:38 === building individual classifier 1, out-of-bag (13/36.1%) === [1] 2024-08-21 01:39:38, oob acc: 80.77%, # of SNPs: 19, # of haplo: 40 === building individual classifier 2, out-of-bag (11/30.6%) === [2] 2024-08-21 01:39:38, oob acc: 90.91%, # of SNPs: 32, # of haplo: 32 === building individual classifier 3, out-of-bag (14/38.9%) === [3] 2024-08-21 01:39:38, oob acc: 89.29%, # of SNPs: 19, # of haplo: 43 === building individual classifier 4, out-of-bag (13/36.1%) === [4] 2024-08-21 01:39:38, oob acc: 84.62%, # of SNPs: 19, # of haplo: 100 === building individual classifier 5, out-of-bag (9/25.0%) === [5] 2024-08-21 01:39:39, oob acc: 94.44%, # of SNPs: 22, # of haplo: 58 === building individual classifier 6, out-of-bag (17/47.2%) === [6] 2024-08-21 01:39:39, oob acc: 79.41%, # of SNPs: 27, # of haplo: 63 === building individual classifier 7, out-of-bag (12/33.3%) === [7] 2024-08-21 01:39:39, oob acc: 75.00%, # of SNPs: 19, # of haplo: 40 === building individual classifier 8, out-of-bag (13/36.1%) === [8] 2024-08-21 01:39:39, oob acc: 80.77%, # of SNPs: 17, # of haplo: 60 === building individual classifier 9, out-of-bag (11/30.6%) === [9] 2024-08-21 01:39:39, oob acc: 86.36%, # of SNPs: 20, # of haplo: 33 === building individual classifier 10, out-of-bag (10/27.8%) === [10] 2024-08-21 01:39:39, 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 (2024-08-21 01:39:39) 0% Predicting (2024-08-21 01:39:39) 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 [-] 2024-08-21 01:39:39 === building individual classifier 1, out-of-bag (11/35.5%) === [1] 2024-08-21 01:39:39, oob acc: 95.45%, # of SNPs: 11, # of haplo: 22 === building individual classifier 2, out-of-bag (11/35.5%) === [2] 2024-08-21 01:39:39, oob acc: 100.00%, # of SNPs: 13, # of haplo: 22 === building individual classifier 3, out-of-bag (15/48.4%) === [3] 2024-08-21 01:39:39, oob acc: 83.33%, # of SNPs: 15, # of haplo: 23 === building individual classifier 4, out-of-bag (14/45.2%) === [4] 2024-08-21 01:39:39, oob acc: 82.14%, # of SNPs: 8, # of haplo: 14 === building individual classifier 5, out-of-bag (13/41.9%) === [5] 2024-08-21 01:39:39, oob acc: 88.46%, # of SNPs: 11, # of haplo: 34 === building individual classifier 6, out-of-bag (10/32.3%) === [6] 2024-08-21 01:39:39, oob acc: 90.00%, # of SNPs: 11, # of haplo: 21 === building individual classifier 7, out-of-bag (13/41.9%) === [7] 2024-08-21 01:39:39, oob acc: 92.31%, # of SNPs: 14, # of haplo: 23 === building individual classifier 8, out-of-bag (13/41.9%) === [8] 2024-08-21 01:39:39, oob acc: 96.15%, # of SNPs: 11, # of haplo: 16 === building individual classifier 9, out-of-bag (14/45.2%) === [9] 2024-08-21 01:39:40, oob acc: 89.29%, # of SNPs: 12, # of haplo: 19 === building individual classifier 10, out-of-bag (11/35.5%) === [10] 2024-08-21 01:39:40, 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 (2024-08-21 01:39:40) 0% Predicting (2024-08-21 01:39:40) 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 [-] 2024-08-21 01:39:40 === building individual classifier 1, out-of-bag (11/32.4%) === [1] 2024-08-21 01:39:40, oob acc: 86.36%, # of SNPs: 13, # of haplo: 34 === building individual classifier 2, out-of-bag (13/38.2%) === [2] 2024-08-21 01:39:40, oob acc: 76.92%, # of SNPs: 21, # of haplo: 42 === building individual classifier 3, out-of-bag (13/38.2%) === [3] 2024-08-21 01:39:40, oob acc: 80.77%, # of SNPs: 10, # of haplo: 17 === building individual classifier 4, out-of-bag (13/38.2%) === [4] 2024-08-21 01:39:40, oob acc: 92.31%, # of SNPs: 22, # of haplo: 78 === building individual classifier 5, out-of-bag (13/38.2%) === [5] 2024-08-21 01:39:40, oob acc: 92.31%, # of SNPs: 11, # of haplo: 40 === building individual classifier 6, out-of-bag (14/41.2%) === [6] 2024-08-21 01:39:40, oob acc: 71.43%, # of SNPs: 8, # of haplo: 22 === building individual classifier 7, out-of-bag (14/41.2%) === [7] 2024-08-21 01:39:40, oob acc: 71.43%, # of SNPs: 14, # of haplo: 53 === building individual classifier 8, out-of-bag (11/32.4%) === [8] 2024-08-21 01:39:40, oob acc: 86.36%, # of SNPs: 14, # of haplo: 40 === building individual classifier 9, out-of-bag (14/41.2%) === [9] 2024-08-21 01:39:40, oob acc: 100.00%, # of SNPs: 16, # of haplo: 56 === building individual classifier 10, out-of-bag (13/38.2%) === [10] 2024-08-21 01:39:41, 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 (2024-08-21 01:39:41) 0% Predicting (2024-08-21 01:39:41) 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 [-] 2024-08-21 01:39:41 === building individual classifier 1, out-of-bag (15/42.9%) === [1] 2024-08-21 01:39:41, oob acc: 70.00%, # of SNPs: 17, # of haplo: 77 === building individual classifier 2, out-of-bag (16/45.7%) === [2] 2024-08-21 01:39:41, oob acc: 56.25%, # of SNPs: 19, # of haplo: 92 === building individual classifier 3, out-of-bag (15/42.9%) === [3] 2024-08-21 01:39:41, oob acc: 70.00%, # of SNPs: 11, # of haplo: 32 === building individual classifier 4, out-of-bag (15/42.9%) === [4] 2024-08-21 01:39:41, oob acc: 73.33%, # of SNPs: 20, # of haplo: 138 === building individual classifier 5, out-of-bag (14/40.0%) === [5] 2024-08-21 01:39:41, oob acc: 75.00%, # of SNPs: 17, # of haplo: 73 === building individual classifier 6, out-of-bag (12/34.3%) === [6] 2024-08-21 01:39:42, oob acc: 66.67%, # of SNPs: 20, # of haplo: 154 === building individual classifier 7, out-of-bag (11/31.4%) === [7] 2024-08-21 01:39:42, oob acc: 63.64%, # of SNPs: 15, # of haplo: 38 === building individual classifier 8, out-of-bag (11/31.4%) === [8] 2024-08-21 01:39:42, oob acc: 68.18%, # of SNPs: 19, # of haplo: 115 === building individual classifier 9, out-of-bag (12/34.3%) === [9] 2024-08-21 01:39:42, oob acc: 83.33%, # of SNPs: 21, # of haplo: 141 === building individual classifier 10, out-of-bag (11/31.4%) === [10] 2024-08-21 01:39:43, 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 (2024-08-21 01:39:43) 0% Predicting (2024-08-21 01:39:43) 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 6.56 0.20 6.76
HIBAG.Rcheck/HIBAG-Ex.timings
name | user | system | elapsed | |
HIBAG-package | 0.32 | 0.06 | 0.40 | |
hlaAllele | 0.01 | 0.00 | 0.03 | |
hlaAlleleDigit | 0 | 0 | 0 | |
hlaAlleleSubset | 0.02 | 0.00 | 0.02 | |
hlaAlleleToVCF | 1.97 | 0.01 | 1.98 | |
hlaAssocTest | 1.03 | 0.05 | 1.08 | |
hlaAttrBagging | 0.31 | 0.03 | 0.36 | |
hlaBED2Geno | 0.06 | 0.02 | 0.09 | |
hlaCheckAllele | 0 | 0 | 0 | |
hlaCheckSNPs | 0.08 | 0.01 | 0.11 | |
hlaCombineAllele | 0.02 | 0.00 | 0.02 | |
hlaCombineModelObj | 0.34 | 0.00 | 0.36 | |
hlaCompareAllele | 0.23 | 0.02 | 0.26 | |
hlaConvSequence | 2.85 | 0.26 | 3.11 | |
hlaDistance | 1.25 | 0.02 | 1.29 | |
hlaFlankingSNP | 0.01 | 0.00 | 0.03 | |
hlaGDS2Geno | 0.07 | 0.01 | 0.09 | |
hlaGeno2PED | 0.04 | 0.00 | 0.08 | |
hlaGenoAFreq | 0 | 0 | 0 | |
hlaGenoCombine | 0.05 | 0.00 | 0.05 | |
hlaGenoLD | 0.59 | 0.04 | 0.62 | |
hlaGenoMFreq | 0.02 | 0.00 | 0.02 | |
hlaGenoMRate | 0 | 0 | 0 | |
hlaGenoMRate_Samp | 0 | 0 | 0 | |
hlaGenoSubset | 0.01 | 0.00 | 0.01 | |
hlaGenoSwitchStrand | 0.04 | 0.01 | 0.05 | |
hlaLDMatrix | 1.61 | 0.11 | 1.75 | |
hlaLociInfo | 0 | 0 | 0 | |
hlaMakeSNPGeno | 0.01 | 0.00 | 0.01 | |
hlaModelFiles | 0.17 | 0.02 | 0.21 | |
hlaModelFromObj | 0.05 | 0.01 | 0.06 | |
hlaOutOfBag | 0.67 | 0.03 | 0.72 | |
hlaParallelAttrBagging | 0.47 | 0.00 | 1.25 | |
hlaPredMerge | 0.28 | 0.02 | 0.33 | |
hlaPredict | 0.22 | 0.00 | 0.22 | |
hlaPublish | 0.33 | 0.00 | 0.32 | |
hlaReport | 0.27 | 0.01 | 0.29 | |
hlaReportPlot | 1.64 | 0.04 | 1.67 | |
hlaSNPID | 0 | 0 | 0 | |
hlaSampleAllele | 0.00 | 0.01 | 0.01 | |
hlaSetKernelTarget | 0 | 0 | 0 | |
hlaSplitAllele | 0.04 | 0.00 | 0.05 | |
hlaSubModelObj | 0.07 | 0.00 | 0.06 | |
hlaUniqueAllele | 0.01 | 0.00 | 0.02 | |
plot.hlaAttrBagObj | 0.25 | 0.02 | 0.28 | |
print.hlaAttrBagClass | 0.06 | 0.03 | 0.09 | |
summary.hlaSNPGenoClass | 0 | 0 | 0 | |