Hi, I'm hoping that someone can help a person new to the lme4 package.
My analysis  should be dead simple but the more I'm reading, the more I'm becoming less clear on what the current recommended approach is for evaluating the significance for fixed-effects.  It seems that lme4 using anova(model)  no longer provides p values that the lme package provided when using anova(model).   I'm not clear on the reason for this.  I'm also under the impression that using a model comparison technique leading to a LRT is misleading for evaluating Fixed-effects as well (from Pinheiro and Bates).  Hummm... any clarity would be appreciated.

The dataset is a remarkably simple repeated measures design with one unbalanced fixed effect;

SUBJECT   TIME  GRAD  PAIN
1              Pre         Yes         9.0
1              Post       Yes         5.0
2              Pre         No          8.6
2              Post       No          6.8
...
52           Pre         Yes         6.3
52           Post       Yes         6.0

(41 'GRAD Yes' SUBJECTS and 11 'GRAD No' SUBJECTS)

Using    model <- lmer(PAIN~1+TIME*GRAD+(1|SUBJECT),data)

Thanks.
Geoff


Geoffrey Schultz, PhD
Registered Psychologist
Alberta Health Services
Chronic Pain Centre


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