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Is the binomial GLMM the right answer?
I cant seem to find a definitive answer on the net without still feeling slightly uncertain, and wish to make sure I am not making a mistake. Any help or insight into alternative methods on this would be much appreciated. The binomial GLMM is probably the right answer.
How to fitting a GLMM to a response variable?
Fitting a binomial GLMM (glmer) to a response variable that is a proportion or fraction. I have some count data as a response variable and I want to measure how that variable changes with the proportional presence of something. In more detail, the response variable is counts of the presence of an insect species in a number of sites,…
How to choose the right type of statistical test?
Nominal: represent group names (e.g. brands or species names). Binary: represent data with a yes/no or 1/0 outcome (e.g. win or lose). Choose the test that fits the types of predictor and outcome variables you have collected (if you are doing an experiment, these are the independent and dependent variables ).
Which is the correct method for fitting a glmer?
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. I cant seem to find a definitive answer on the net without still feeling slightly uncertain, and wish to make sure I am not making a mistake.
Is the GLMMs an extension of generalized linear regression?
Alternatively, you could think of GLMMs as an extension of generalized linear models (e.g., logistic regression) to include both fixed and random effects (hence mixed models). The general form of the model (in matrix notation) is:
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.
Is the interpretation of GLMMs the same as GLMs?
The interpretation of GLMMs is similar to GLMs; however, there is an added complexity because of the random effects. On the linearized metric (after taking the link function), interpretation continues as usual. However, it is often easier to back transform the results to the original metric.