Modeling correlation structure in mixed models
West et al. Linear Mixed Models. Chapman & Hall. This text uses SAS, SPSS, HLM and R to set up and solve a variety of hierarchical mixed models. The text uses 'nlme' exclusively, but the related website has 'lme4' equivalent scripts.
At 06:42 PM 6/26/2009, Phillip Chapman wrote:
Hi All, I have been trying to learn mixed models in R by reading the books by Pinheiro and Bates; Faraway (both linear models books); and Crawley (R Book), but I would appreciate some guidance from the more experience R users. (I have a fair amount of experience with mixed models in SAS.) 1. Is there another (other than the above) suggested reference for understanding the workings of the nlme and lme4 libraries? 2. Is it the case that lme accepts correlated structures ONLY in the error term? I have problems in which I would like model random effects (such as year) using a random term with an autocorrelated structure. In SAS I use options to the "repeated" statement to add correlation structure to the error term, and I use options to the "random" statement to give correlation structure to the other random effects. I haven't found anything in lme or lmer that allows me to specify correlated random effects. gee only allows correlation structure in the error term and does not allow random effects. 3. All of the examples of random effects in lme seem to have nested error structures. Is it the case that lme does not allow crossed random effects? lmer allows much more flexible specification of random effects, but I don't see anything that allows correlated error structures. Thanks in advance, Phil Chapman
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