hlaOutOfBag {HIBAG} | R Documentation

## Out-of-bag estimation of overall accuracy, per-allele sensitivity, etc

### 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

    # 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")

[Package HIBAG version 1.16.0 Index]