Dear list,
I am currently working with a rather large data set on body temperature
regulation in wintering birds. My original model contains quite a few
dependent variables, but I do not (of course) wish to keep them all in my
final model. I've fitted the following model to the data:
Have you thought about using AIC weights? As long as you are not considering
models where you drop your random effects, calculating AIC values (or AICc
values) and doing multimodel inference is one way to approach your problem.
If you are fitting models with and without random effects, it gets
movies (used to) say: just a friendly warning ... :)
-- Bert Gunter
Genentech
- Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On
Behalf Of Andreas Nord
Sent: Monday, August 25, 2008 9:22 AM
To: r-help@r-project.org
Subject: [R] lmer4 and variable selection
Dear
On Mon, 25 Aug 2008, jebyrnes wrote:
Have you thought about using AIC weights? As long as you are not considering
models where you drop your random effects, calculating AIC values (or AICc
values) and doing multimodel inference is one way to approach your problem.
If you are fitting models
] [mailto:[EMAIL PROTECTED]
On
Behalf Of Andreas Nord
Sent: Monday, August 25, 2008 9:22 AM
To: r-help@r-project.org
Subject: [R] lmer4 and variable selection
Dear list,
I am currently working with a rather large data set on body temperature
regulation in wintering birds. My original model
5 matches
Mail list logo