[R-sig-ME] different output in R and SAS for GLMM

[Ben Bolker]

PS it looks like you're using this ... ?


On Tue, Dec 29, 2015 at 12:01 AM, Ben Bolker <bbolker at gmail.com> wrote:
> May we have a *reproducible* example please?  (All of your R code,
> plus either your data set or a similar (or smaller) data set with the
> SAS results corresponding to it ...)
>
> On Mon, Dec 28, 2015 at 11:56 PM, Adeela Munawar <adeela.uaf at gmail.com> wrote:
>> Dear all,
>>
>> I am comparing the output of SAS and R for generalized linear mixed models
>> with gamma family. Code for SAS are
>>
>> proc glimmix data=ch12_ex1 plot=residualpanel(ilink) noprofile;
>>  class block a b;
>>  model days=a|b / d=gamma;
>>  random intercept a/subject=block;
>>  lsmeans a*b / slicediff=(a b) ilink cl;
>>  covtest /cl(type=plr);
>>
>> while I am fitting this using lme4 package as
>> a<-factor(a)
>> b<-factor(b)
>> block<-factor(block)
>>
>> ModelGamma <- glmer(days~a*b+(1|block/a),family=Gamma(link = "log"))
>>  lsmeans(ModelGamma,~a*b)
>>
>> but the results are altogether different. SAS gives 9 df while NA in R and
>> least sqaure means are also different.
>>
>> a b   lsmean        SE      df   asymp.LCL asymp.UCL
>>  1 1 3.212923 0.5677001 NA  2.100251  4.325595
>>  2 1 3.229803 0.5662713 NA  2.119932  4.339675
>>  3 1 3.279271 0.5688496 NA  2.164346  4.394196
>>  1 2 2.499457 0.5679181 NA  1.386358  3.612556
>>  2 2 3.248968 0.5659105 NA  2.139804  4.358132
>>  3 2 3.563672 0.5689044 NA  2.448640  4.678705
>>
>> Why this happens? Please suggest.
>>
>> Thanks,
>> Adeela
>>
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>>
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