Contents
Should I use Glmer or LMER?
The lmer() function is for linear mixed models and the glmer() function is for generalized mixed models. Returns a model object of class merMod. The merMod object is a list of objects which result from fitting the model. For generalized mixed models the familyName sets the link and variance function for the model.
What is Reml in statistics?
In statistics, the restricted (or residual, or reduced) maximum likelihood (REML) approach is a particular form of maximum likelihood estimation that does not base estimates on a maximum likelihood fit of all the information, but instead uses a likelihood function calculated from a transformed set of data, so that …
When to use lmer in binomial data analysis?
In the text book “The R Book”, (2007), pg 604, Crawley suggests using the lmer function with a binomial family for the analysis of binomial data where each participant contributes multiple responses (analagous to each of my chambers contributing multiple outcomes). Based on this example, I have used the following script for my data:
When to use glmer or GLM for binomial outcomes?
You can think of random intercepts as being part of the experimental design. On the other hand, if the random effect variance is very small and/or the inference or predictions for both the glm and glmer model are the largely the same, then it really doesn’t matter which you use anyway. For 2, yes, you should use glmer or glm for binomial outcomes.
Which is the correct method for fitting a GLMM?
I thought of two methods, one would be an linear model ( lmer) with the insects converted to a proportion e.g. The second would be a binomial GLMM ( glmer ) e.g. I believe the binomial glmer to be the correct method, however they produce fairly different results.
Can a GLM be used to fit a binary model?
3) glm can fit a model for binary data without random effects. However, it is incorrect to compare a model fitted with glm to one fitted with glmer using a likelihood-based test because the likelihoods are not comparable.