[R-sig-ME] Fwd: glme output

[Ben Bolker]

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On 15-06-08 11:23 AM, Daniel Frese wrote:
> 

  [snip]

> I am in the process of learning R, and converting over from SAS.
> 
> I have the following model and I am having a difficult time finding
> code that will get me the lumens output of the model.  What I am
> looking for is an equivalent command to the SAS code of  LSMEANS
> within the PROC GLIMMIX.
> 
> Model is the following
> 
> fm14bf<-glmer(I(Steps+1)~Treatment*BFStrata*treathour  + time hour
> + (1|Block) + (0+treatdate|Steer), family=poisson,data=datahourly)
> 
> summary(fm14bf,ddf="Kenward-Roger") qqnorm(resid(fm14bf),main="QQ
> Model 14") plot(fm14bf,main="Residual Model 14") 
> hist(residuals(fm14bf))
> 
> 
> How would I go about getting LSMEANS and differences output from R
> 
> Thanks
> 
> Dan
> 
> Daniel Frese DVM Graduate Teaching Assistant Beef Cattle Institute 
> College of Veterinary Medicine Kansas State University


I would suggest that you take a look at the 'lsmeans' and 'effects'
packages for R -- I'm pretty sure they can both handle glmer models.

Some further questions/suggestions about your model:

* I'm not sure that summary(...,ddf="Kenward-Roger") is going to
do anything at all for a glmer model -- presumably you are using
the lmerTest package, which augments the summary method in this
way, but relies on the pbkrtest package, which does *not* implement
K-R for GLMMs (Stroup 2014 says that despite the lack of theoretical
grounding K-R *does* seem to give reasonable results for GLMMs).

* why are you using Steps+1 as your response variable in a Poisson model?
That seems odd -- a Poisson model *should* be able to handle Steps==0
responses perfectly well.  (I think you can actually get away without
I() for a model _response_)

* do you have a good reason to suppress the intercept variation among
Steers, i.e. are the starting values for each Steer constrained to
be exactly identical?

* you might want to consider adding an observation-level random effect
to allow for overdispersion ...

- ----
Stroup, Walter W. “Rethinking the Analysis of Non-Normal Data in Plant
and Soil Science.” Agronomy Journal 106 (2014):
1–17. doi:10.2134/agronj2013.0342.








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