R: Multiple QTL AIC

mAIC {QTLRel} | R Documentation

## Multiple QTL AIC

### Description

Multiple QTL model selection by AIC criterion.

### Usage

    mAIC(y, x, gdat, prdat = NULL, vc = NULL, chrIdx, xin, k = 2,
       direction = c("both","backward","forward"), ext = FALSE, msg = FALSE)

### Arguments

`y` | A numeric vector or a numeric matrix of one column (representing a phenotype for instance).
`x` | A data frame or matrix, representing covariates if not missing.
`gdat` | Genotype data. Should be a matrix or a data frame, with each row representing an observation and each column a marker locus. The column names should be marker names. Numeric coding of genotype is treated as numeric. Ignored if `prdat` is an object from `genoProb`.
`vc` | An object from `estVC` or `aicVC`, or an estimated variance-covariance matrix induced by relatedness. The scan will assume no polygenic variation if `vc` is NULL.
`prdat` | An object from `genoProb`.
`chrIdx` | Chromsome index of markers in columns of `gdat` if given. Ignored if `prdat` is an object from `genoProb`.
`xin` | Vector indicating whether a locus is already in the model.
`k` | Penalty on a parameter. The selection criterion is the known "AIC" if `k = 2` and is "BIC" if `k = log(n)` where "n" is the sample size.
`direction` | The mode of search: "both", "forward" or "backward" with default "both".
`ext` | A logical variable. True if ones wants more exhaustive search.
`msg` | A logical variable. True if ones wants to track the process for monitoring purpose.

### Details

Makes use of "Haley-Knott" method (Haley and Knott 1992) if `prdat` is an object from `genoProb`.

### Value

A list with the following components:

`model`: the resulting model;

`aic`: AIC of the model;

`snp`: selected SNPs.

`xin`: vector indicating whether a SNP is selected.

### Note

Currently only suitable for advanced intercross lines (or diallelic data).

### References

Haley, C. S., and S. A. Knott (1992). A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69: 315-324.

### See Also

`optim`, `genoProb` and `aicVC`.

### Examples

    data(miscEx)

    ## Not run:
    # impute missing genotypes
    pheno<- pdatF8[!is.na(pdatF8$bwt) & !is.na(pdatF8$sex),]
    ii<- match(rownames(pheno), rownames(gdatF8))
    geno<- gdatF8[ii,]
    ii<- match(rownames(pheno), rownames(gmF8$AA))
    v<- list(A=gmF8$AA[ii,ii], D=gmF8$DD[ii,ii])

    gdat.imp<- genoImpute(geno, gmap=gmapF8, step=Inf,
       gr=8, na.str=NA)
    # estimate variance components
    o<- estVC(y=pheno$bwt, x=pheno$sex, v=v)

    # run 'genoProb'
    gdtmp<- geno
       gdtmp<- replace(gdtmp,is.na(gdtmp),0)
    prDat<- genoProb(gdat=gdtmp, gmap=gmapF8, step=Inf,
       gr=8, method="Haldane", msg=TRUE)

    # genome scan
    llk.hk<- scanOne(y=pheno$bwt, x=pheno$sex, prdat=prDat, vc=o)
    xin<- llk.hk$p > 10

    # run 'mAIC' based on genome scan results
    mg<- mAIC(y=pheno$bwt, x=pheno$sex, prdat=prDat, vc=o, xin=xin,
       k=5, direction="back", msg=TRUE)
    mg$model$value # likelihood of the final model

    ## End(Not run)

[Package QTLRel version 1.0 Index]