[R-sig-ME] Is there a way to deal with errors such as this?

Jonathan Judge b@ch|@w01 @end|ng |rom out|ook@com
Sun Dec 15 02:49:45 CET 2019


Sadly, that error has always been the kiss of death for me no matter what I tried. As parameterized, the model probably just won’t optimize in lme4. If you want to stick with pseudo-frequentist inference, you could try glmmTMB, which is developed by some of the same folks (well, one in particular) and it may work using the same notation you are used to. But that error message usually means you need to try a different package. 


Sent from my iPhone

> On Dec 14, 2019, at 7:21 PM, Rolf Turner <r.turner using auckland.ac.nz> wrote:
> By "this" I mean as demonstrated in the following code.  The file testData.txt is attached.
> X <- dget("testData.txt")
> library(lme4)
> fit <- glmer(cbind(Dead,Alive) ~ (0+Trt)/Dose + (Dose | Rep),
>             data=X,family=binomial(link="probit"))
> The foregoing falls over with the (rather complex) error message:
>> Error in pwrssUpdate(pp, resp, tol = tolPwrss, GQmat = GHrule(0L), compDev = compDev,  :   (maxstephalfit) PIRLS step-halvings failed to reduce deviance in pwrssUpdate
> I note that mixed_model() from GLMMadaptive seems to be able to deal with these data and this model:
> library(GLMMadaptive)
> fit <- mixed_model(fixed=cbind(Dead,Alive) ~ (0+Trt)/Dose,
>                   random=~Dose | Rep,
>                   data=X,family=binomial(link="probit"))
> The foregoing runs without complaint.
> I am applying the glmer() model in the context of doing some fairly elaborate simulations (in which "X" gets randomly generated) and the error causes the simulations to crash unpleasantly.  So I would *like* a magic incantation that I can apply in an automated way to prevent the
> error from occurring.
> I can of course wrap function calls up in try() and if there is an error
> generate a new data set and go again.  However I'm a little apprehensive
> that this might bias the results of the simulations in some way.
> I could also switch to using mixed_model(), but would prefer to stick with the devil I know (i.e. glmer()) for the sake of consistency with other work that I have done.  (And who knows?  Maybe in the course of the simulations mixed_model() might fall over too, from time to time.)
> I'd appreciate any avuncular (or materteral) advice that anyone might be inclined to offer.
> cheers,
> Rolf
> -- 
> Honorary Research Fellow
> Department of Statistics
> University of Auckland
> Phone: +64-9-373-7599 ext. 88276
> <testData.txt>
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