Hello,
I've just located the illuminating explanation by Douglas Bates on degrees
of freedom in mixed models.
The take-home message appears to be: don't trust the p-values from lme.
Questions:
Should I give up hypothesis testing for fixed effects terms in mixed models?
Has my time spent reading
Try using mcmcsamp() to sample from the posterior distribution of the
parameter estimates. You can calculate a p-value from that, if that is
your desire. Instructions are in the R wiki:
http://wiki.r-project.org/rwiki/doku.php?id=guides:lmer-tests
HTH,
Simon.
Dan Bebber wrote:
Hello,
: Fri 5/19/2006 7:54 PM
To: Frank E Harrell Jr
Cc: Douglas Bates; r-help
Subject:Re: [R] lmer, p-values and all that
On Fri, 2006-05-19 at 17:44 -0500, Frank E Harrell Jr wrote:
Douglas Bates wrote:
Users are often surprised and alarmed that the summary of a linear
. . . .
Doug
Users are often surprised and alarmed that the summary of a linear
mixed model fit by lmer provides estimates of the fixed-effects
parameters, standard errors for these parameters and a t-ratio but no
p-values. Similarly the output from anova applied to a single lmer
model provides the sequential
Douglas Bates wrote:
Users are often surprised and alarmed that the summary of a linear
. . . .
Doug,
I have been needing this kind of explanation. That is very helpful.
Thank you. I do a lot with penalized MLEs for ordinary regression and
logistic models and know that getting sensible
On Fri, 2006-05-19 at 17:44 -0500, Frank E Harrell Jr wrote:
Douglas Bates wrote:
Users are often surprised and alarmed that the summary of a linear
. . . .
Doug,
I have been needing this kind of explanation. That is very helpful.
Thank you. I do a lot with penalized MLEs for ordinary
