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
On 11 Aug 2010, at 15:31, Ben Bolker wrote:

On 10-08-11 10:21 AM, Chris Mcowen wrote:

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 
 ...