[R-lang] p-values for factors using lmer & mcmc

[Kathryn Campbell-Kibler]

Hi all,

I recently upgraded versions of R and the package lme4 in a fit of
something-or-other.  My older version was old enough to still have
p-values.  I now have the current version (0.99875-7), and am learning
to use mcmc to calculate p-values, but basically all my independent
variables are factors, so p-values for each level are not really
helpful, I need to estimate the impact of the whole factor. I can live
without p-values on the model itself, but the lack of them in anova is
killing me. I've been trying out mcmcpvalue from here:

http://wiki.r-project.org/rwiki/doku.php?id=guides:lmer-tests

But it seems to only work for the linear models, not the glmms.  Is
there something out there for those, or a way to adapt this script for
it?

Also, I'm not really understanding the structure well enough to get
how to get it to evaluate interactions (or if it will).  If I'm
looking at something like this:

> HPDinterval(status_samp)
                                                  lower       upper
(Intercept)                                  3.96106199  4.54810253
sregionivan                                 -1.49569838 -0.32674386
sregionjason                                -1.08346050  0.06535123
sregionsouth                                -0.96097370 -0.13712609
ininging                                    -0.15327601  0.19483823
factor(workingclass)1                       -1.40345872 -0.53510599
sregionivan:ininging                        -0.28212347  0.48140623
sregionjason:ininging                        0.00660252  0.72089386
sregionsouth:ininging                       -0.34948297  0.17119827
sregionivan:factor(workingclass)1            0.19306970  1.56657035
sregionjason:factor(workingclass)1          -0.13693835  2.36486351
sregionsouth:factor(workingclass)1           0.10013804  1.11524003
ininging:factor(workingclass)1               0.60875674  1.78589051
sregionivan:ininging:factor(workingclass)1  -2.36086950 -0.34788386
sregionjason:ininging:factor(workingclass)1 -2.16553468  0.84887041
sregionsouth:ininging:factor(workingclass)1 -1.65891153 -0.26497955
log(sigma^2)                                -0.56604768 -0.37239636
log(id.(In))                                -2.44938452 -1.67803069
log(word.(In))                              -2.24918742 -0.96898869
attr(,"Probability")
[1] 0.95

which lines together give me the interaction of ining and
factor(workingclass)?  Is it just

ininging:factor(workingclass)1               0.60875674  1.78589051

or is it

ininging                                    -0.15327601  0.19483823
factor(workingclass)1                       -1.40345872 -0.53510599
ininging:factor(workingclass)1               0.60875674  1.78589051

One tempting option is to look at the p-values for anova comparing two
models, one with and one without the term or interaction I'm
interested in.  But searching on the R-help list tells me that's not a
good idea, as it is anti-conservative.  Can someone explain why, or
point me to a good explanation (where good=using small non-technical
words)?

Any help is much appreciated.

Thanks,

Kathryn