print.hlaAttrBagClass | 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)
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