hlaOutOfBag: Out-of-bag estimation of overall accuracy, per-allele...

In HIBAG: HIBAG -- HLA Genotype Imputation with Attribute Bagging

Description

Out-of-bag estimation of overall accuracy, per-allele sensitivity, specificity, positive predictive value, negative predictive value and call rate.

Usage

hlaOutOfBag(model, hla, snp, call.threshold=NaN, verbose=TRUE)

Arguments

model	an object of hlaAttrBagClass or hlaAttrBagObj
hla	the training HLA types, an object of hlaAlleleClass
snp	the training SNP genotypes, an object of hlaSNPGenoClass
call.threshold	the specified call threshold; if NaN, no threshold is used
verbose	if TRUE, show information

Value

Return hlaAlleleClass.

Author(s)

Xiuwen Zheng

See Also

hlaCompareAllele, hlaReport

Examples

# load HLA types and SNP genotypes
data(HLA_Type_Table, package="HIBAG")
data(HapMap_CEU_Geno, package="HIBAG")

# make a "hlaAlleleClass" object
hla.id <- "A"
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")

# 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")
length(snpid)  # 275

# training and validation genotypes
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 a HIBAG model
set.seed(100)
# please use "nclassifier=100" when you use HIBAG for real data
model <- hlaAttrBagging(hla, geno, nclassifier=4)
summary(model)

# out-of-bag estimation
(comp <- hlaOutOfBag(model, hla, geno, call.threshold=NaN, verbose=TRUE))

# report
hlaReport(comp, type="txt")

hlaReport(comp, type="tex")

hlaReport(comp, type="html")

HIBAG documentation built on May 2, 2019, 4:50 p.m.