hlaReport {HIBAG} | R Documentation |
Create a report for evaluating prediction accuracies.
hlaReport(object, export.fn="", type=c("txt", "tex", "html", "markdown"), header=TRUE)
object |
an object returned by |
export.fn |
a file name for output, or "" for |
type |
|
header |
if |
None.
Xiuwen Zheng
# make a "hlaAlleleClass" object hla.id <- "A" hla <- hlaAllele(HLA_Type_Table$sample.id, H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")], H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")], locus=hla.id, assembly="hg19") # divide HLA types randomly set.seed(100) hlatab <- hlaSplitAllele(hla, train.prop=0.5) names(hlatab) # "training" "validation" summary(hlatab$training) summary(hlatab$validation) # SNP predictors within the flanking region on each side region <- 500 # kb snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id, HapMap_CEU_Geno$snp.position, hla.id, region*1000, assembly="hg19") length(snpid) # 275 # training and validation genotypes train.geno <- hlaGenoSubset(HapMap_CEU_Geno, snp.sel = match(snpid, HapMap_CEU_Geno$snp.id), samp.sel = match(hlatab$training$value$sample.id, HapMap_CEU_Geno$sample.id)) test.geno <- hlaGenoSubset(HapMap_CEU_Geno, samp.sel=match(hlatab$validation$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(hlatab$training, train.geno, nclassifier=4, verbose.detail=TRUE) summary(model) # validation pred <- predict(model, test.geno) # compare (comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model, call.threshold=0)) # report hlaReport(comp, type="txt") hlaReport(comp, type="tex") hlaReport(comp, type="html") hlaReport(comp, type="markdown")