Dear all, Consider a completely randomized block design (let's use data(Oats) irrespoctive of the split-plot design it was arranged in). Look:
library(nlme) fit <- lme(yield ~ nitro, Oats, random = ~1|Block, method="ML") fit2 <- lm(yield ~ nitro + Block, Oats) anova(fit, fit2) gives this: Model df AIC BIC logLik Test L.Ratio p-value fit 1 4 624.3245 633.4312 -308.1623 fit2 2 8 611.9309 630.1442 -297.9654 1 vs 2 20.39366 4e-04 Clearly, considering block a random term is worse than considering it a fixed term. Let's see if blocking should be included in the model at all: fit3 <- lm(yield ~nitro, Oats) anova(fit2,fit3) which gives a very small P value in favor of fit2, which suggests the block term should be included. So, I go for the second model, with block considered fixed. Is this indeed how I should generally proceed when choosing the optimum model for a situation that calls for mixed effects? Of course, the example above is overly simplistic, yet such situations can occur -- from a complex model with a couple of random terms one can finally get to a simple fixed-effects model. Please comment. Thanks in advance, Stats Wolf ______________________________________________ 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.