This function is not meant to be called by end-users,
although technically-minded users can call this function
for flexibility beyond what the other functions in this
package provide. See lmBF for a
user-friendly front-end to this function. Details about
the priors can be found in the help for
anovaBF and the references therein. Arguments struc and gMap provide a way of
grouping columns of the design matrix as a factor; the
effects in each group will share a common $g$
parameter. Only one of these arguments is needed; if both
are given, gMap takes precedence.
gMap should be a vector of the same length as the
number of nonconstant rows in X. It will contain
all integers from 0 to $N_g-1$, where
$N_g$ is the total number of $g$ parameters.
Each element of gMap specifies the group to which
that column belongs.
If all columns belonging to a group are adjacent,
struc can instead be used to compactly represent
the groupings. struc is a vector of length
$N_g$. Each element specifies the number columns
in the group. gMap is thus the
inverse.rle of struc, minus 1.
The vector rscale should be of length
$N_g$, and contain the prior scales of the
standardized effects. See Rouder et al. (2012) for more
details and the help for anovaBF for some
typical values.
The method used to estimate the Bayes factor depends on
the method argument. "simple" is most accurate for
small to moderate sample sizes, and uses the Monte Carlo
sampling method described in Rouder et al. (2012).
"importance" uses an importance sampling algorithm with
an importance distribution that is multivariate normal on
log(g). "laplace" does not sample, but uses a Laplace
approximation to the integral. It is expected to be more
accurate for large sample sizes, where MC sampling is
slow. integration, and the posterior is sampled with a
Gibbs sampler.