From: Feldman, Tracy <tsfeldman_at_noble.org>

Date: Wed, 16 Jan 2008 11:44:59 -0600

R-help_at_r-project.org mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Wed 16 Jan 2008 - 17:49:05 GMT

Date: Wed, 16 Jan 2008 11:44:59 -0600

I used lmer for data with non-normally distributed error and both fixed
and random effects. I tried to calculate a "Type III" sums of squares
result, by I conducting likelihood ratio tests of the full model against
a model reduced by one variable at a time (for each variable
separately). These tests gave appropriate degrees of freedom for each of
the two fixed effects, but when I left out one of two random effects

(each random effect is a categorical variable with 5 and 8 levels,

respectively) and tested that reduced model against the full model,
output showed that the test degrees of freedom = 1, which was incorrect.
Since I used an experimental design with spatial and temporal
"blocks"-where I repeated the same experiment several times, with a
different treatments in each spatial block each time (and with different
combinations of individuals in each treatment)-I am now thinking that I
should leave the random effects in the model no matter what (and only
test for fixed effects). This leaves me with three related questions:

- Why do Likelihood Ratio Tests of a full model against a model with one less random effect report the incorrect degrees of freedom? Are such tests treating each random variable as one combined entity? I can provide code and data if this would help.
- In a publication, is it reasonable to report that I only tested models that included random effects? Do I need to report results of a test of significance of these random effects (i.e., I am not sure how or if I should include any information about the random effects in my "ANOVA-type" tables)?
- If I should test for the significance of random effects, per se

(and report these), is it more appropriate to simply fit models with and

without random effects to see if the pattern of fixed effects is different? I can look at random effects using "ranef(model_name)", but this function does not assess their significance.

I am not subscribed to this list, so if possible, please reply to me directly at tsfeldman_at_noble.org . Thank you for your time and help.

Sincerely,

Tracy Feldman

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Wed 16 Jan 2008 - 17:49:05 GMT

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