Multiple QTL AIC
Multiple QTL model selection by AIC criterion.
mAIC(y, x, gdat, prdat = NULL, vc = NULL, chrIdx, xin, k = 2,
direction = c("both","backward","forward"), ext = FALSE, msg = FALSE)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 |
vc |
An object from |
prdat |
An object from |
chrIdx |
Chromsome index of markers in columns of |
xin |
Vector indicating whether a locus is already in the model. |
k |
Penalty on a parameter. The selection criterion is the known "AIC" if |
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. |
Makes use of "Haley-Knott" method (Haley and Knott 1992) if prdat is an object from genoProb.
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.
Currently only suitable for advanced intercross lines (or diallelic data).
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.
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$DD[ii,ii]) gdat.imp<- genoImpute(geno, gmap=gmapF8, gr=8, na.str=NA) # estimate variance components o<- estVC(y=pheno$bwt, x=pheno$sex, v=v) # run 'genoProb' gdtmp<- geno gdtmp<- replace(gdtmp,is.na(gdtmp),0) prDat<- genoProb(gdat=gdtmp, gmap=gmapF8, gr=8, method="Haldane", msg=TRUE) # genome scan llk.hk<- scanOne(y=pheno$bwt, x=pheno$sex, prdat=prDat, vc=o) xin<- llk.hk$LRT > 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)
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