View source: R/DataUtilities.R
hlaModelFiles | 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
hlaAttrBagging
, hlaModelToObj
# 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)
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