Hi all, I?ve written a function which some might find useful. It outputs ?corrected? Pearson residuals from a glmer fit of the form fit <- glmer(y ~ . + (1 | obs), family = ?poisson?) where "(1 | obs)? fits an observation level random effect (OLRE), i.e. length(obs) == length(y).* The function is called residfitted.olre and can be found here: https://github.com/pcdjohnson/miscR The default behaviour of residuals(fit) and fitted(fit) is to treat the OLRE as part of the fitted values, but for most practical purposes I want the OLRE to be in the residuals and not in the fitted values. In particular, if I?m using a residuals-vs-fitted-values plot to assess the fit, the default residuals and fitted functions used by plot(fit) will generally produce nasty-looking plots showing a trend and severe heteroscedasticity even from well fitting models. The ?corrected? residuals fix this problem. There?s also example code below the function showing how this it allows interpretable residuals-vs-fitted plots to be produced from a Poisson GLMM with an OLRE (aka a Poisson-lognormal GLMM). The same problem affects a binomial GLMM with an OLRE, but I haven?t (yet) made the function work for binomial GLMMs for the reason covered in this post: https://stat.ethz.ch/pipermail/r-sig-mixed-models/2014q1/021818.html (basically I don?t know what the variance function is for a mixture of binomial + logit-normal ? anyone know?). Hope someone finds it useful, Paul *Refs: https://peerj.com/articles/616/ http://www.ncbi.nlm.nih.gov/pubmed/11393830
Function to get residuals & fitted values from Poisson GLMM with OLRE
1 message · Paul Johnson