On Thu, 21 Aug 2008, Christoph Scherber wrote:
Dear all,
Thanks to Brian Ripley for pointing this out. If I understand it correctly,
this would mean that looking at the parameter estimates, standard errors and
P-values in summary.lme only makes sense if no interaction terms are present?
You
Christoph Scherber wrote:
> Dear all,
>
> Thanks to Brian Ripley for pointing this out. If I understand it
> correctly, this would mean that looking at the parameter estimates,
> standard errors and P-values in summary.lme only makes sense if no
> interaction terms are present?
Yes and no. What it
Dear all,
Thanks to Brian Ripley for pointing this out. If I understand it correctly, this would mean that
looking at the parameter estimates, standard errors and P-values in summary.lme only makes sense if
no interaction terms are present?
My conclusion would then be that it is better to rel
Please read the help for anova.lme, and note the 'type' argument. You are
comparing apples and oranges here (exactly as if you did this for a linear
model fit).
Because you have a three-way interaction in your model, looking at the
(marginal) t-tests for any other coefficient than the third-o
Dear all,
When analyzing data from a climate change experiment using linear mixed-effects
models, I recently
came across a situation where:
- the summary(model) showed a significant difference between the levels of a
two-level factor,
- while the anova(model) showed no significance for that fa
5 matches
Mail list logo
