| 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, ...)
reg.formula |
A formula.object like y ~ add.PVV4 *
add.H15C12 . The names of the independent
variables on the right hand side of the formula are the names of loci
or the names of additive and dominance terms associated with loci. In
addition, one can use locus or configs terms to specify
one or a collection of terms in a shorthand notation. See
locus for more details. The left hand side is the name
of a trait variable stored in the search path, as a column of the
data frame data, or y if the phenotype variable in
ana.obj is used. |
ana.obj |
The result of make.analysis.obj . |
... |
Arguments to pass to lapadj, e.g. rparm and
return.hess |
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)