Sorry to be blunt, but to make the statement that it is unacceptable without providing any reason why the model may fail to converge seems a little presumptious. Your statement really bothers me, especially knowing how hard the developer works on keeping this function at a premier level. Ask anyone working with mixed models and they will all agree that Doug Bates and this function are shining stars in the world of computational statistics. 

R is open source, and therefore you are certainly welcome and encouraged to add to its functionality if you wish. Basically, one may argue that you are in a "put up, or shut up" position. Feel free to write a nice piece of code based on MQL that we can use (and critique).

But even more importantly, in order to provide any help to you at all, you should provide sample data and examples of your code. We may find that the problem is the user and not the function. 

I generally disagree with rudeness on this list, and agree that my tone is not very agreeable, but please read the posting guide and follow basic protocol by describing your problem, create sample data, illustrate your code, and then ask for help.

Harold
 


-----Original Message-----
From:	r-help-bounces@stat.math.ethz.ch on behalf of Roel de Jong
Sent:	Tue 11/1/2005 6:05 PM
To:	r-help
Cc:	
Subject:	Re: [R] glmmpql and lmer keep failing

Formulated more directly, are there plans for the implementation of the 
crude but more robust Marginal Quasi Likelihood estimation in for 
example LME?

Regards,
	Roel de Jong

Roel de Jong wrote:
> Hello,
> 
> I'm running a simulation study of a multilevel model with binary 
> response using the binomial probit link. It is a random intercept and 
> random slope model.  GLMMPQL and lmer fail to converge on a 
> *significant* portion of the *generated* datasets, while MlWin gives 
> reasonable estimates on those datasets. This is unacceptable. Does 
> anyone has similar experiences?
> 
> Regards,
>     Roel de Jong
>

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