hlaModelFiles {HIBAG} | R Documentation |
To load HIBAG models from a list of files, and merge all together.
hlaModelFiles(fn.list, action.missingfile=c("ignore", "stop"), verbose=TRUE)
fn.list |
a vector of file names |
action.missingfile |
"ignore", ignore the missing files, by default; "stop", stop if missing |
verbose |
if TRUE, show information |
Return hlaAttrBagObj
.
Xiuwen Zheng
# make a "hlaAlleleClass" object hla.id <- "C" hla <- hlaAllele(HLA_Type_Table$sample.id, H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")], H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")], locus=hla.id, assembly="hg19") # training genotypes region <- 100 # kb snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id, HapMap_CEU_Geno$snp.position, hla.id, region*1000, assembly="hg19") train.geno <- hlaGenoSubset(HapMap_CEU_Geno, snp.sel = match(snpid, HapMap_CEU_Geno$snp.id), samp.sel = match(hla$value$sample.id, HapMap_CEU_Geno$sample.id)) # # train HIBAG models # set.seed(1000) model1 <- hlaAttrBagging(hla, train.geno, nclassifier=1) mobj1 <- hlaModelToObj(model1) save(mobj1, file="tm1.RData") model2 <- hlaAttrBagging(hla, train.geno, nclassifier=1) mobj2 <- hlaModelToObj(model2) save(mobj2, file="tm2.RData") model3 <- hlaAttrBagging(hla, train.geno, nclassifier=1) mobj3 <- hlaModelToObj(model3) save(mobj3, file="tm3.RData") # load all of mobj1, mobj2 and mobj3 mobj <- hlaModelFiles(c("tm1.RData", "tm2.RData", "tm3.RData")) summary(mobj) # delete the temporary files unlink(c("tm1.RData", "tm2.RData", "tm3.RData"), force=TRUE)