[R-sig-ME] [R-sig-eco] LRT tests in lmer

[Jarrod Hadfield]

Hi,

In general I don't think transforming the fixed effect predictions by  
the inverse link  function works if you want to get the predicted  
expectation.  In this case you have to take into account the magnitude  
of the variance components.  The new predict function in MCMCglmm will  
do this for a MCMCglmm fit. By default the predictions will be on the  
data scale and all random effects marginalised, but you can also get  
predictions that include the random effects if you save their  
posterior distribution (i.e pr=TRUE)

Cheers,

Jarrod




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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