Dear all,
Thanks for the responses to this post.
I understand that the topic still requires more research. However, I am a non-statistician in a
desperate need to analyze my ecological data with the currently available tools. Please excuse
again my non-expert question: Would I commit a huge
On Wed, 20 Apr 2005, Nestor Fernandez wrote:
Dear all,
Thanks for the responses to this post.
I understand that the topic still requires more research. However, I am a
non-statistician in a desperate need to analyze my ecological data with the
currently available tools. Please excuse again my
Hello All:
Should I conclude from this discussion that there is no practical
means by which nested generalized mixed models can be compared from
output produced through glmmPQL or GLMM? What is one then to do???
Andrew
On Sun, 17 Apr 2005, Deepayan Sarkar wrote:
On Sunday 17 April 2005
Andrew Criswell asks:
Hello All:
Should I conclude from this discussion that there is no
practical
means by which nested generalized mixed models can be compared
from
output produced through glmmPQL or GLMM?
[WNV] The picture is, in my view, not as bleak as
[EMAIL PROTECTED]
Subject:Re: [R] generalized linear mixed models - how to
compare?
Copies to: r-help@stat.math.ethz.ch,
Nestor Fernandez [EMAIL PROTECTED]
On Sun, 17 Apr 2005, Deepayan Sarkar wrote:
On Sunday 17 April 2005 08:39, Nestor Fernandez wrote:
I
: Prof Brian Ripley [EMAIL PROTECTED]
To: Deepayan Sarkar [EMAIL PROTECTED]
Subject:Re: [R] generalized linear mixed models - how to
compare?
Copies to: r-help@stat.math.ethz.ch,
Nestor Fernandez [EMAIL PROTECTED]
On Sun, 17 Apr 2005, Deepayan
On Sunday 17 April 2005 08:39, Nestor Fernandez wrote:
Dear all,
I want to evaluate several generalized linear mixed models, including
the null model, and select the best approximating one. I have tried
glmmPQL (MASS library) and GLMM (lme4) to fit the models. Both result
in similar
On Sun, 17 Apr 2005, Deepayan Sarkar wrote:
On Sunday 17 April 2005 08:39, Nestor Fernandez wrote:
I want to evaluate several generalized linear mixed models, including
the null model, and select the best approximating one. I have tried
glmmPQL (MASS library) and GLMM (lme4) to fit the models.
On Sunday 17 April 2005 12:07, Prof Brian Ripley wrote:
On Sun, 17 Apr 2005, Deepayan Sarkar wrote:
[...]
GLMM uses (mostly) the same procedure to get parameter estimates,
but as a final step calculates the likelihood for the correct model
for those estimates (so the likelihood reported
