print.hlaAttrBagClass {HIBAG} | R Documentation

## Summarize a “hlaAttrBagClass” or “hlaAttrBagObj” object.

### Description

Summarize an object of `hlaAttrBagClass` or `hlaAttrBagObj`.

### Usage

    ## 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, ...)

### Arguments

`x` | an object of `hlaAttrBagClass` or `hlaAttrBagObj`
`object` | an object of `hlaAttrBagClass` or `hlaAttrBagObj`
`show` | if TRUE, show information
`...` | further arguments passed to or from other methods

### Value

`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 `data.frame` for the average number of SNPs (`num.snp`), haplotypes (`num.haplo`), out-of-bag accuracies (`accuracy`) among all classifiers: mean, standard deviation, min, max

### Author(s)

Xiuwen Zheng

### See Also

`plot.hlaAttrBagClass`, `plot.hlaAttrBagObj`

### Examples

    # 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)

[Package HIBAG version 1.24.0 Index]