Hello,
I've been running some HLMs using the lme function quite happily; it
does what I want and I'm pretty sure I understand it.
The issue is that I'm currently trying to estimate a model with a
14-level "nusiance" factor as an independent variable...which makes the
output quite ugly. All I'm really interested in is the question of whether
these factor as a whole (and its interactions with other factors) are
significant.
The summary.aov function provides this sort of aggregation for lm
objects, but does not run on lme objects. I've also tried estimating the
full model and restricted model, leaving out a main effect or interaction
term and then using anova.lme to compare the models, but these models appear
to be being fit differently. Say I have l2, and then
l3 <- update(l2, .~.-useful:nusience)
anova.lme(l3,l2)
...to see whether the interaction term is significant, produces the error,
"Fitted objects with different fixed effects. REML comparisons are not
meaningful." Upon examination using summary(l3), it seems that the fixed
factors are indeed different.
So, my question is this: How do I estimate omnibus main effects for
multi-level factors and multi-level factor interactions in lme models?
Many thanks,
Adam D. I. Kramer
Ph.D. Student, Social and Personality Psychology
University of Oregon
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