Hello, everyone,
I am not very familiar to use R.
I used SAS Proc GLIMMIX to estimate R- and G-side random effects of
generalized linear mixed models. But the procedure can just use PQL or MQL.
Now I want to lmer function in R to estimate GLMM because it can use Laplace
approximation. It is more precise than PQL or MQL. But I read the manual of
lmer. I did not find the information how to write a covariance structure in
the function and how the function can know the struction is for R- or G-side
random effects.
For example, if I use the following SAS proc GLIMMIX code to estimate a
model, I wonder who can tell me how to use lmer to write R code to estimate
the same model.
proc glimmix ;
class person group item;
model score(event='1')=item group /noint dist=binary link=logit s;
random _residual_ / sub=person type=cs ;
run;
Thanks a lot!
P. T. Shu
how to estimate R- and G-side random effects of GLMM in R
2 messages · ts p, Douglas Bates
On 9/16/07, ts p <pts007 at hotmail.com> wrote:
Hello, everyone,
I am not very familiar to use R.
I used SAS Proc GLIMMIX to estimate R- and G-side random effects of
generalized linear mixed models. But the procedure can just use PQL or MQL.
Now I want to lmer function in R to estimate GLMM because it can use Laplace
approximation. It is more precise than PQL or MQL. But I read the manual of
lmer. I did not find the information how to write a covariance structure in
the function and how the function can know the struction is for R- or G-side
random effects.
For example, if I use the following SAS proc GLIMMIX code to estimate a
model, I wonder who can tell me how to use lmer to write R code to estimate
the same model.
proc glimmix ;
class person group item;
model score(event='1')=item group /noint dist=binary link=logit s;
random _residual_ / sub=person type=cs ;
run;
I am not fluent in SAS but I believe the model that you want to fit would be lmer(score ~ item + group + (1|person), <dataSetName>, family = binomial) assuming that score, item, person and group are stored as factors and that score has only two levels. If not you should dichotomize score before fitting the model. Others may be better able to decide what model the SAS code would fit. I have difficulty with this because, for example, I can't see what the compound symmetry structure is supposed to mean. There certainly isn't a compound symmetry structure on either the marginal or the conditional distribution of the response given the random effects, because the variance of a binomial depends on the mean. Specifying an R matrix independently of the linear predictor doesn't make sense to me.