linear.bayes {bqtl}  R Documentation 
The Bayesian QTL models via a likelihood that is linearized w.r.t. a fixed genetic model. By default, all one and two gene models (without epistasis) are fitted and a MCMC sampler is used to fit 3,4, and 5 gene and (optionally) larger models.
linear.bayes(x, ana.obj, partial=NULL, rparm, specs,
scope, subset, casewt, ...)
x 
a formula giving the QTL and the candidate loci or a

ana.obj 
An analysis.object, see 
partial 
a formula giving covariates to be controlled 
rparm 
A ridge parameter. A value of 1 is suggested, but the default is 0. 
specs 
An optional list with components

scope 
Not generally used. If supplied this will be passed to

subset 
Not generally used. If supplied this will be passed to

casewt 
Not generally used. If supplied this will be passed to

... 
optional arguments to pass to 
This function is a wrapper for
varcov
, twohk
, swap
, and
summary.swap
, and a better understanding of optional
arguments and the object generated is gained from their
documentation.
hk 
The object returned by 
swaps 
A list of objects returned by calls to

smry 
A list of objects returned by calls to

odds 
A Vector of odds (relative to a no gene setup) for each
model size evaluated. The odds are computed under a prior that
places equal weights on models of each size considered (and are,
therefore, Bayes Factors). If models of size 1 and 2 are not
evaluated or if some degenerate results were encountered, this will
be 
coefs 
A vector of posterior means of the regression
coefficients. If models of size 1 and 2 are not
evaluated or if some degenerate results were encountered, this will
be 
loc.posterior 
A vector of locuswise posterior probabilities
that the interval covered by this locus contains a gene.If models of
size 1 and 2 are not evaluated or if some degenerate results were
encountered, this will be 
call 
The call that generated this object 
Charles C. Berry cberry@ucsd.edu
Berry C.C.(1998) Computationally Efficient Bayesian QTL Mapping in Experimental Crosses. ASA Proceedings of the Biometrics Section. 164–169.
data( little.ana.bc )
little.lin < linear.bayes( bc.phenotype~locus(all), little.ana.bc, rparm=1 )
par(mfrow=c(2,3))
plot( little.ana.bc, little.lin$loc.posterior, type="h" )
little.lin$odds
par(mfrow=c(1,1))
plot(fitted(little.lin), residuals(little.lin))