[R-sig-ME] Follow-up question
Cátia Ferreira De Oliveira
cm|o500 @end|ng |rom york@@c@uk
Wed May 19 03:19:01 CEST 2021
Thank you for your reply!
I have run the allfit() function and it has shown some inconsistency in the
results. Are there any practical issues with running glmer(logRT) other
than interpretability? I have run simpler models but they still complain
about convergence. Even those with just a random intercept.
> A quarta, 19/05/2021, 01:52, Ben Bolker <bbolker using gmail.com> escreveu:
>> Good meta-question.
>> You can check the archives at
>> https://stat.ethz.ch/pipermail/r-sig-mixed-models/ to make sure you
>> haven't missed a reply.
>> Unfortunately, since this is an all-volunteer forum, there's no
>> particular guarantee that people will remain interested in a thread/get
>> around to answering your questions. IMO it's reasonable etiquette to
>> "bump"/remind readers about a question that has been languishing for
>> some time (once you've allowed a reasonable time for an answer, e.g. a
>> few days); it may be helpful to include a link, in this case
>> https://stat.ethz.ch/pipermail/r-sig-mixed-models/2021q2/029477.html ,
>> to help people re-locate the question.
>> It would be OK to try your luck in another forum such as
>> CrossValidated; it would be polite and useful if/when you post there to
>> provide a link to this thread so people can see what has already been
>> discussed (although you should also provide a good summary there, so
>> readers don't *have* to come trawl through the archives).
>> A few more points since I'm responding:
>> * From your _original_ problem; I've updated the influence.merMod()
>> method in the *development* version of lme4 so that it doesn't choke
>> when nAGQ=0 (the starting value can be set manually, but it also checks
>> and does the right thing by default when nAGQ is 0). If you're able to
>> installed packages with compiled code from source
>> (remotes::install_github("lme4/lme4")), you should be able to run
>> influence() on your original model.
>> * The gold standard for whether convergence warnings are really a
>> problem or not is allFit(). If you run allFit(), and the results from a
>> range of optimizers **are sufficiently similar to each other for your
>> scientific purposes**, then you can feel free to disregard convergence
>> warnings (at that point it doesn't really matter how nasty the warnings
>> * You _might_ try simplifying the model a little bit, e.g. removing the
>> Session : Probability interaction in the random effects term (the
>> standard deviation for that component is 5 times smaller than the
>> intercept variation, and 29 times smaller than the residual variation
>> One tiny final point: please don't use the salutation "Dear Professor
>> Bolker" when responding to the list - even though I might be answering
>> your question at the moment, the query is still to the whole list.
>> Ben Bolker
>> On 5/18/21 3:10 PM, Cátia Ferreira De Oliveira wrote:
>> > Hello,
>> > I am sorry for bothering you but I have asked a follow-up question and
>> > haven't obtained a response, I wonder if it will ever be responded to
>> > I don't know if there is any way of keeping track of which questions
>> > or not answered. If not, I may need to try my luck on crossvalidated as
>> > it is quite a timely problem that I am experiencing with the models.
>> > After removing the "logRT" from the glmer model I got a lot more
>> > convergence issues, so I am left wondering if having the logRT would be
>> > problematic. If so, would there be other options for me on what to do
>> > be able to model this data.
>> > Best wishes,
>> > Catia
>> > --
>> > Cátia Margarida Ferreira de Oliveira
>> > Psychology PhD Student
>> > Department of Psychology, Room B214
>> > University of York, YO10 5DD
Cátia Margarida Ferreira de Oliveira
Psychology PhD Student
Department of Psychology, Room B214
University of York, YO10 5DD
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