p values with lmer (mcmcpvalue error)

That works, thanks! ?Though it only provides p-values for each factor,
not for each level of each factor. ?This may be sufficient, but in the
past I've seen p-values presented along with the estimates and
t-values for each level of each factor (ex. for Act=Feed, Act=Rest ...
, not just for Act) ?Any way to do this?

On 4/16/11, Douglas Bates <bates at stat.wisc.edu> wrote:

On Sat, Apr 16, 2011 at 4:58 AM, Kaitlyn Gaynor <kaitlyng at gmail.com> wrote:

Hello,

I am currently using lmer to make a fairly large model with 9 fixed
factors (8 of them with 2-4 levels, and 1 of them continuous) and 2
random factors, >1000 observations. ?I realize there is a lot of
debate about p-values for parameter estimates but they?ve been
requested by a journal so I?m trying to calculate them. ?I have been
trying to obtain p-values using the mcmcpvalue function:

fit<-lmer(vig~Act+Height+FD+IGE+Pos.For+PR+Kin+PDDO+Rank+(1|Grp/Subj),
data=vigilance)
mcmcpvalue <- function(samp)
{ std <- backsolve(chol(var(samp)),
cbind(0, t(samp)) - colMeans(samp),
transpose = TRUE)
sqdist <- colSums(std * std)
sum(sqdist[-1] > sqdist[1])/nrow(samp) }
markov1 <- mcmcsamp(fit, 5000)
HPDinterval(markov1)

mcmcpvalue(as.matrix(markov1[,1]))
mcmcpvalue(as.matrix(markov1[,2]))
...

HPDinterval generates CIs for each treatment, but mcmcpvalue function
produces the following error messages:

Error in markov1[, 1] : object of type 'S4' is not subsettable
Error in as.matrix(markov1[, 1]) :
?error in evaluating the argument 'x' in selecting a method for
function 'as.matrix'

Any help would be greatly appreciated!

With over 1000 observations I would feel comfortable quoting p-values
from likelihood ratio tests. ?First switch to the maximum likelihood
(ML) estimation method rather than REML estimation then fit the model
without a particular term and evaluate the likelihood ratio (which is
somewhat confusingly produced by the anova() function). ?That is

fitML <- update(fit, REML=FALSE)
fit1 <- update(fit, . ~ . - Act)
anova(fit1, fitML)
...