hlaCombineModelObj: Combine two HIBAG models together

In HIBAG: HLA Genotype Imputation with Attribute Bagging

Description

Merge two objects of hlaAttrBagObj together, which is useful for building an ensemble model in parallel.

Usage

hlaCombineModelObj(obj1, obj2)

Arguments

obj1	an object of hlaAttrBagObj
obj2	an object of hlaAttrBagObj

Value

Return an object of hlaAttrBagObj.

Author(s)

Xiuwen Zheng

See Also

hlaAttrBagging, hlaModelFiles

Examples

# 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 genotypes
train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
    snp.sel = match(snpid, HapMap_CEU_Geno$snp.id))

# train a HIBAG model
set.seed(100)
m1 <- hlaAttrBagging(hla, train.geno, nclassifier=1)
m2 <- hlaAttrBagging(hla, train.geno, nclassifier=1)

m1.obj <- hlaModelToObj(m1)
m2.obj <- hlaModelToObj(m2)

m.obj <- hlaCombineModelObj(m1.obj, m2.obj)
summary(m.obj)

Example output

HIBAG (HLA Genotype Imputation with Attribute Bagging)
Kernel Version: v1.3
Supported by Streaming SIMD Extensions (SSE2) [64-bit]
[1] 275
Remove 9 monomorphic SNPs
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
Tue Apr  2 22:16:08 2019,   1 individual classifier, out-of-bag acc: 86.96%, # of SNPs: 12, # of haplo: 32
Remove 9 monomorphic SNPs
Build a HIBAG model with 1 individual classifier:
# of SNPs randomly sampled as candidates for each selection: 17
# of SNPs: 266, # of samples: 60
# of unique HLA alleles: 14
Tue Apr  2 22:16:08 2019,   1 individual classifier, out-of-bag acc: 87.50%, # of SNPs: 15, # of haplo: 40
Gene: A
Training dataset: 60 samples X 266 SNPs
	# of HLA alleles: 14
	# of individual classifiers: 2
	total # of SNPs used: 24
	average # of SNPs in an individual classifier: 13.50, sd: 2.12, min: 12, max: 15
	average # of haplotypes in an individual classifier: 36.00, sd: 5.66, min: 32, max: 40
	average out-of-bag accuracy: 87.23%, sd: 0.38%, min: 86.96%, max: 87.50%
Genome assembly: hg19

HIBAG documentation built on March 24, 2021, 6 p.m.