bqtl {bqtl} | R Documentation |
Find maximum likelihood estimate(s) or posterior mode(s) for QTL model(s). Use Laplace approximation to determine the posterior mass associated with the model(s).
bqtl(reg.formula, ana.obj, scope = ana.obj$reg.names, expand.specials = NULL, ...)
reg.formula |
A formula.object like |
ana.obj |
The result of |
scope |
passed to |
expand.specials |
passed to |
... |
Arguments to pass to |
This function is a wrapper for lapadj
. It does a lot
of useful packaging through the configs
terms. If there
is no configs
term, then the result is simply the output of
lapadj
with the call
attribute replaced by the
call to bqtl
The result(s) of calling lapadj
.
If configs
is used in the reg.formula
, then the
result is a list with one element for each formula. Each element is the
value returned by lapadj
Charles C. Berry cberry@ucsd.edu
Tierney L. and Kadane J.B. (1986) Accurate Approximations for Posterior Moments and Marginal Densities. JASA, 81,82–86.
data(little.ana.bc ) # load BC1 dataset loglik( bqtl( bc.phenotype ~ 1, little.ana.bc ) ) #null loglikelihood #on chr 1 near cM 25 loglik(bqtl(bc.phenotype~locus(chromo=1,cM=25),little.ana.bc)) little.bqtl <- # two genes with epistasis bqtl(bc.phenotype ~ m.12 * m.24, little.ana.bc) summary(little.bqtl) several.epi <- # 20 epistatic models bqtl( bc.phenotype ~ m.12 * locus(31:50), little.ana.bc) several.main <- # main effects only bqtl( bc.phenotype ~ m.12 + locus(31:50), little.ana.bc) max.loglik <- max( loglik(several.epi) - loglik(several.main) ) round( c( Chi.Square=2*max.loglik, df=1, p.value=1-pchisq(2*max.loglik,1)) ,2) five.gene <- ## a five gene model bqtl( bc.phenotype ~ locus( 12, 32, 44, 22, 76 ), little.ana.bc , return.hess=TRUE ) regr.coef.table <- summary(five.gene)$coefficients round( regr.coef.table[,"Value"] + # coefs inside 95% CI qnorm(0.025) * regr.coef.table[,"Std.Err"] %o% c("Lower CI"=1,"Estimate"=0,"Upper CI"=-1),3)