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

[Jarrod Hadfield]

Hi,

Could you give summary(model) with the new version (2.05) - it will be  
easier to see what is going on?

Jarrod
On 11 Aug 2010, at 17:08, Chris Mcowen wrote:

> 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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>
>
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The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.