print.hlaAttrBagClass {HIBAG} | R Documentation |
Summarize an object of hlaAttrBagClass
or
hlaAttrBagObj
.
## S3 method for class 'hlaAttrBagClass' print(x, ...) ## S3 method for class 'hlaAttrBagObj' print(x, ...) ## S3 method for class 'hlaAttrBagClass' summary(object, show=TRUE, ...) ## S3 method for class 'hlaAttrBagObj' summary(object, show=TRUE, ...)
x |
an object of |
object |
an object of |
show |
if TRUE, show information |
... |
further arguments passed to or from other methods |
print
returns NULL
.
summary.hlaAttrBagClass
and summary.hlaAttrBagObj
return
a list
:
num.classifier |
the total number of classifiers |
num.snp |
the total number of SNPs |
snp.id |
SNP IDs |
snp.position |
SNP position in basepair |
snp.hist |
the number of classifier for each SNP, and it could be used for SNP importance |
info |
a |
Xiuwen Zheng
plot.hlaAttrBagClass
, plot.hlaAttrBagObj
# 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)) # train a HIBAG model set.seed(1000) # please use "nclassifier=100" when you use HIBAG for real data model <- hlaAttrBagging(hla, train.geno, nclassifier=2, verbose.detail=TRUE) print(model)