Chi Yuan <cyuan <at> email.arizona.edu> writes > > Hello: > I need some help about using mixed for model for unbalanced data. I > have an two factorial random block design. It's a ecology > experiment. My two factors are, guild removal and enfa removal. Both > are two levels, 0 (no removal), 1 (removal). I have 5 blocks. But > within each block, it's unbalanced at plot level because I have 5 > plots instead of 4 in each block. Within each block, I have 1 plot > with only guild removal, 1 plot with only enfa removal, 1 plot for > control with no removal, 2 plots for both guild and enfa removal. I am > looking at how these treatment affect the enfa mortality rate. I > decide to use mixed model to treat block as random effect. So I try > both nlme and lme4. But I don't know whether they take the unbalanced > data properly. So my question is, does lme in nlme and lmer in lme4 > take unbalanced data? How do I know it's analysis in a proper way?
Didn't Bert Gunter and I already provide answers to this question last week? Can you please clarify what about those answers you didn't understand? > Another question is about p values. > I kind of heard the P value does not matter that much in the mixed > model because it's not calculate properly. Is there any other way I can > tell whether the treatment has a effect not? I know AIC is for model > comparison, > do I report this in formal publication? It is indeed hard to compute p-values, but ... if you use AIC, you are essentially making the same assumption as if you assumed that the denominator degrees of freedom were infinite in an F test (or if you used the likelihood ratio test). [snip results] Ben Bolker ______________________________________________ R-help@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.