[R-sig-ME] glmer vs glmmPQL vs glmmadmb

[Magdalena Wiedermann]

Thank you Ben!

Indeed there is a problem and I keep coming back to this dater after 
having learned more about GLMM on other projects. I tried the suggested 
Gauss-Hermite quadrature. The higher the nAGQ number the more 
interactions become significant in the summary output. The car::Anova 
output stays the same though. I have a lot of Zero values both in one of 
my explanatory variables as well as in about half of my response values. 
Adding 1 and log transforming both of them (which is admittedly 
questionable in terms of elegance) reduces the importance of the 
interaction term but loosens up the cluster in the residual plots. 
During a previous attempt I had talked to Dave about using a zero 
inflation model, but we had concluded that this is not the way to go. 
Now I don't know where to go, because my residual plots are always 
highly clustered around the many 0 (1) values.

Thank you
Lena


On 04/04/2016 11:51 PM, Ben Bolker wrote:
>    If glmer and glmmADMB agree with each other and disagree with
> glmmPQL, I would generally trust the former (Laplace approximation is
> better than PQL, esp for binary data).  However, (1) you should also
> try Gauss-Hermite quadrature (nAGQ>1) in glmer, and (2) the very large
> magnitude of your parameters makes it look like you probably have a
> complete-separation problem ...
>
>
> On Mon, Apr 4, 2016 at 10:53 PM, Magdalena Wiedermann <mwiederm at mtu.edu> wrote:
>> Dear List
>>
>> Quick question: Why is the interaction term usingglmmPQL not
>> significant, whereas it is highly significant using glmer and glmmadmb?
>>
>> Thank you!
>> Lena
>>
>> *_
>> Example:_*
>>
>> resp = resp<-cbind(data$Dead, data$Alive)
>>
>> m1<-glmer(resp~(treatm+log(Tree))^2+block+(1|plot), family=binomial,
>> data = data)
>>
>> summary(m1)
>>
>>
>> m2<-glmmPQL(resp~(treatm+log(Tree))^2+block, random=~1|plot,
>> family=binomial, data=data)
>>
>> summary(m2)
>>
>>
>> |m3<-|glmmadmb|(||resp||~||(||treatm||+||log(Tree)||)^2||+block,
>> random=~1|plot,||||family=||"||binomial||"||, ||data=||data||)|
>>
>> |summary(m3) |*_Results:_*
>>
>>   > car::Anova(m1)
>> Analysis of Deviance Table (Type II Wald chisquare tests)
>>
>> Response: resp
>>                                     Chisq Df Pr(>Chisq)
>> treatm                        82.6249  4     <2e-16 ***
>> log(Tree)                  17992.6841  1     <2e-16 ***
>> block                          3.6873  5     0.5953
>> treatm:log(Tree) 230.6844 4 <2e-16 ***
>>
>>
>>   >car::Anova(m2)
>> Analysis of Deviance Table (Type II tests)
>>
>> Response: resp
>>                                   Chisq Df Pr(>Chisq)
>> treatm                       114.4015  4     <2e-16 ***
>> log(Tree)                    384.7095  1     <2e-16 ***
>> block                          1.0899  5     0.9550
>> treatm:log(Tree) 2.9839 4 0.5605
>>
>>   > car::Anova(m3)
>> Analysis of Deviance Table (Type II tests)
>>
>> Response: resp
>>                                 Df     Chisq Pr(>Chisq)
>> treatm                        4   68.7297  4.208e-14 ***
>> log(Tree)                     1 1846.9933  < 2.2e-16 ***
>> block                         5    3.0464     0.6928
>> treatm:log(Tree) 4 117.7358 < 2.2e-16 ***
>> Residuals                    734
>>
>>
>> p.s.: this it true for summary(m1,m2,m3) too.
>>
>>
>>
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>>
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