Dear list members, I fitted a linear mixed effects model using the lme function from nlme package. In the model I included two fixed effects, one being continuous and one a factor having 4 categories. Furthermore, I have one random effect (id) which I want to include as a random intercept only. I used the following code:
fit1 <- lme(outcome ~ fixed1 + fixed2, random = ~1|id) Now I would like to perform a lack-of-fit test. In a previous post (where there was only one continuous fixed effect) I have seen the following suggestion: fit1 <- lme(outcome ~ fixed1, random = ~1|id, method="ML") fit2 <- lme(outcome ~ factor(fixed1, ordered=TRUE), random = ~1|id, method="ML") anova(fit1,fit2) Now my questions are: 1) How do I perform a lack-of-fit test with one continuous and one factor as fixed affects? 2) Is it necessary to set method=ML for the lack-of-fit test? 3) If I have to use method=ML for the lack-of-fit test, should I use method=ML also in my model taht I would like to interpret? In my original model I used REML, but only because this is the default in lme and I did not change it. As I have not applied a lack-of-fit test before, I would really be glad for any help! Best regards, John ______________________________________________ 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.
