LRT tests in lmer

Hi Jarrord,

I have tried using MCMCglmm, however the posterior distributions of  
the majority of the fixed factors straddle 0, which i have read is a  
problem, likely with the priors.

HPDintervals - https://files.me.com/chrismcowen/wqq1lu

prior=list(R=list(V=1, fix=1), G=list(G1=list(V=1, nu=0),  
G2=list(V=1, nu=0)))

So i am unsure how to interpret the results, as to ascertain the  
importance of each factor.

Unfortunately i don't know enough about baysian statistics or R to  
alter my model so the interpretations become clearer.

An example

                             			lower      		upper
(Intercept)             			-3.510792767 	2.40740650
STOStorage organ        	-0.299408836 	0.23073133
BSUnisexual flower      	-0.131660436 	0.54887912
BSUnisexual plant       	 0.003566637 	0.81742862
PDBiotic                			 0.054625970 	0.72436838
PDMammalia              		-2.139720264 	1.39753939



On 11 Aug 2010, at 16:37, Jarrod Hadfield wrote:

Hi Chris,

It is hard to say as it will depend on the fixed effects. In  
addition its not clear whether such a situation is diagnostic of a  
problem.  Imagine you just have an intercept which is estimated to  
be exactly zero. The residuals on the data scale will be either 0.5  
or -0.5, but this does not imply the model is wrong.

Cheers,

Jarrod

On 11 Aug 2010, at 15:41, Chris Mcowen wrote:

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 ...

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