LRT tests in lmer

Thats great thanks,

But will this work where you have a binary response variable or will the residuals clump around 1 and 0?

Chris

Dear Ben/Rob.

As far as I can tell, the standard advice is simply to look at the predictions of the model, compare them with the data, and try to spot any systematic patterns in the residuals.

I have plotted the residuals of my model - https://files.me.com/chrismcowen/v586vx

I have been made aware that  that lmer uses the random effects in its  prediction ( Jarrord Hadfield). And this is reflected in the residual plot with the the long lines of equal residuals all belonging  to the same family - i.e 200 - 600 is the orchid family and 650-100 is the grass family.

So is there a work around with a glmm?



Thanks

Chris

If you want to do population-level predictions from a GLMM (i.e. setting all random effects to zero), the basic recipe is to (1) construct a model (design) matrix for the desired sets of predictor variables (if you want to the predict the observed data rather than some other set, you can just extract the model matrix from the fitted object); (2) multiply it by the vector of fixed effect coefficients; (3) transform it back to the scale of the observations with the inverse link function.  There's an example on p. 6 of http://glmm.wdfiles.com/local--files/examples/Owls.pdf ...

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