[R-sig-ME] Strange mcmcsamp issue
Jarrett Byrnes
jebyrnes at ucdavis.edu
Fri Feb 22 01:43:50 CET 2008
I'm attempting to pull out the simple effects from a mixed model with
two crossed treatments. The model structure is such that
a.lmer<-lmer(response ~ trta*trtb+(1|pot))
In the experiment, I have an array of pots. Each pot has a type of
treatment A applied to it. Within the pot, there are two types of
treatment B that are applied, one on either side. I am using a mixed
model as I wanted to account for non-independence within a pot.
There is an interaction between a and b, but I want to look at the 95%
credible intervals of the simple effects to see which treatment
combinations overlap 0, are greater than 0, or are less than 0. While
mcmcsamp works great on this object I am unclear on how to then
combine parameter values and error to get this interval.
So, I attempted a model such as the following
a.lmer<-lmer(response ~ trta*trtb+(1|pot))
Which yielded the following error:
Leading minor of order 15 in downdated X'X is not positive definite
Thinking that this might be an intercept issue, I fit the following
model:
a.lmer<-lmer(response ~ trta*trtb+0+(1|pot))
This fit just fine. summary() showed me a table of parameter values
that seemed about what I would expect, although the correlation of
fixed effects matrix was populated largely by 0's However,
a.mcmcsamp<-(a.lmer, 1000)
yielded the following error
Error: Omega[[1]] is not positive definite
Error in t(.Call(mer_MCMCsamp, object, saveb, n, trans, verbose,
deviance)) :
error in evaluating the argument 'x' in selecting a method for
function 't'
However, if I try for roughly 30 or fewer replicates, everything works
just fine.
Even more strange, when I next looked at a.lmer using summary() all of
the error values for parameters had become 0, and the matrix for
correlation of fixed effects was filled with NaNs. This strikes me as
rather odd.
1) Perhaps this has been fixed in later releases - I'm working off of
lme4 version 0.99875-9 on R 2.6.2. Should I try these instead?
2) Or, am I going about this attempt to get simple effect estimates
all wrong? Is it possible to use the output from the first model with
mcmcsamp to get estimates of the simple effects?
Thanks for any advice you might have!
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