Post-hoc test and Cook's distance vs. leverageplot for glmer from package lme4
Mario Garrido <gaiarrido at ...> writes:
Hi, My name's Mario Garrido, a postdoctoral student in Biology. I am relatively new with r and despite I find it brilliant I am having some difficulties in finding some functions and interpreting the syntaxes. At the moment I am working on a GLZM model which fits a Poisson distribution. I am having some problems with two issues 1. after calculating Akaike weights, my best model is one including a 4-way interaction term. It is the following: model7<-glmer(active~ treatment*daytype*time*age+(1| indiv),family=poisson(link=log),nAGQ=1) Is there any function or package to perform a Post hoc test to know which subset of 2- and 3-way interaction terms have more influence on the model?
It strikes me that this is a somewhat difficult question conceptually, as well as computationally. How are you dealing with the issues of marginality? (See Venables "Exegeses on linear models", available by internet-searching, for a discussion of marginality ...) In other words, how do you define what a 2- or 3-way interaction term means? *If* you can define what you mean (e.g. if simply setting the parameters related to a specific lower-level interaction to zero makes biological or scientific sense), then you could drop the terms and look at the difference in AIC or log-likelihood, and use some sort of multiple comparisons to deal with the post-hocness of it all.
2. In addition, I find how to compute Cook's distance both for the function glmer and for the function lmer using the package influence.ME. This package also allow to make some graphs but, is there any package or function to do it and obtain a plot similar to this: https://climateaudit.files.wordpress.com/2012/09/lew_cooks-distance1.png?
Have you found a way to compute (or define) leverage for a GLMM? (Maybe that's what you're asking for.) Ben Bolker