bicreg.models {BayesQTLBIC} | R Documentation |
bicreg.models
evaluates posterior probabilities based on
the BIC criterion and prior probabilities for a fixed set of models,
typically obtained by sampling from sets of models from separate analyses
of individual chromosomes.
bicreg.models(x,y,wt = rep(1, length(y)),which,intercept=TRUE,add.null.model=TRUE, n=length(y)/num.imputations,num.imputations=1,delta=1, p.sg=1,prior=0.5,eval.markers=TRUE,neval=NULL)
x |
Matrix of independent variables, based on marker genotypes, often from a single chromosome. |
y |
Vector of values for the dependent variable (trait values). |
wt |
Vector of weights for regression. |
which |
Matrix of logical values corresponding to a set of models, the (i,j) element is TRUE if and only if the jth variable is selected in the ith model. |
intercept |
Add an intercept term. |
add.null.model |
Add the NULL model. |
n |
Original sample size, before multiple imputations. |
num.imputations |
Number of imputations used to construct x, y. |
prior |
Vector or scalar specifying prior probabilities per marker for a QTL to be in the vicinity of the marker; generally proportional to the distance to flanking markers and total number of QTL expected genome. Defaults to 0.5 which is usually too high. |
delta |
Adjustment factor for the penalty term in the BIC criterion,
default is no adjustment delta=1; (Cf. Broman and Speed 2002); not
needed if using subjective prior probabilities and sample size is
ample (p.sg=1 and n >= 100; Ball 2007).
|
p.sg |
Proportion p.sg/2 of each tail is genotyped if selective
genotyping is being used; default 1, corresponding to fully
genotyped population. |
eval.markers |
Evaluate model averaged estimates for marker effects (effects of allelic substitution). |
neval |
Use neval top models on which to evaluate model averaged
estimates of marker effects, default NULL, use all models. |
Provides posterior probabilities for a fixed set of linear models representing alternative QTL genetic architectures. Provides Bayesian model averaged estimates for effects of QTL or effects of allelic substitution for markers which may be linked to QTL.
bicreg.models
returns an object of class
bicreg.qtl
.
R.D. Ball, (rod.ball@AT@scionresearch.com)
Ball, R. D. 2001: Bayesian methods for QTL mapping based on model selection: approximate analysis using the Bayesian Information Criterion. Genetics 159: 1351–1364.
bicreg.qtl
,sample.bicreg.qtl.models
## Not run: data(ex3n300a.data) chrom <- rep(1:12,rep(16,12)) marker <- rep(1:16,12) x <- sapply(ex3n300a.data$Markers,c) y <- ex3n300a.data$Trait$t1 nchrom <- length(sort(chrom.levels <- unique(chrom))) quick.demo <- TRUE if(quick.demo){ nc <- 2; nsim <- 20;x <- x[,chrom %in% 1:2]; chrom <- chrom[chrom %in% 1:2] }else{nc <- 12; nsim <- 200} chrom.fits <- list() for(ii in seq(along=chrom.levels[1:nc])){ cat(paste("*** chromosome",ii,"***","\n")) ci <- chrom.levels[ii] chrom.sel <- chrom==ci chrom.fits[[ii]] <- bicreg.qtl(x[,chrom.sel],y, prior=0.1,nbest=20,nvmax=3) } mWhich <- sample.bicreg.qtl.models(chrom.fits,nsim=nsim) mres <- bicreg.models(x=x,y=y,which=mWhich,prior=0.1) summary(mres,nbest=38,min.marker.prob=0.05) ## End(Not run)