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

[Jarrod Hadfield]

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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