hlaPredMerge {HIBAG} | R Documentation

## Merge prediction results from multiple HIBAG models

### Description

Return an object of `hlaAlleleClass`, which contains predicted HLA types.

### Usage

    hlaPredMerge(..., weight=NULL, equivalence=NULL)

### Arguments

`...` | The object(s) of `hlaAlleleClass`, having a field of 'postprob', and returned by `predict(..., type="response+prob", vote="majority")`
`weight` | the weight used for each prediction; if `NULL`, equal weights
`equivalence` | a `data.frame` with two columns, the first column for new equivalent alleles, and the second for the alleles possibly existed in the object(s) passed to this function

### Details

Calculate a new probability matrix for each pair of HLA alleles, by averaging (posterior) probabilities from all models with specified weights. If `equivalence` is specified, multiple alleles might be collapsed into one class.

### Value

Return a `hlaAlleleClass` object.

### Author(s)

Xiuwen Zheng

### See Also

`hlaAttrBagging`, `hlaAllele`, `predict.hlaAttrBagClass`

### 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")

    # 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 HIBAG models
    set.seed(100)

    # please use "nclassifier=100" when you use HIBAG for real data
    m1 <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=2,
        verbose.detail=TRUE)
    m2 <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=2,
        verbose.detail=TRUE)

    # validation
    pd1 <- predict(m1, test.geno, type="response+prob", vote="majority")
    pd2 <- predict(m2, test.geno, type="response+prob", vote="majority")

    hlaCompareAllele(hlatab$validation, pd1)$overall

    hlaCompareAllele(hlatab$validation, pd2)$overall

    # merge predictions from multiple models, by voting from all classifiers
    pd <- hlaPredMerge(pd1, pd2, weight=c(1,1))

    hlaCompareAllele(hlatab$validation, pd)$overall

[Package HIBAG version 1.16.0 Index]