Message-ID: <12e405f9-0223-267b-0e4c-7e8669465f58@gmail.com>
Date: 2020-03-24T00:50:50Z
From: Ben Bolker
Subject: [FORGED] warning error question
In-Reply-To: <0e69f527-be8f-e1a0-8e9f-6cd4eeef8b20@auckland.ac.nz>
On 2020-03-23 6:33 p.m., Rolf Turner wrote:
>
> On 24/03/20 3:41 am, Anah? Fern?ndez wrote:
>
>> hi!! I run this model in lme4:
>> "M.4=glm(Cuenta~carga*categ.asoc+(1|campo/foto)
>> ?????????? +offset(log(area.foto)),family=poisson(link =
>> "log"),data=tipocat)"
>> And I have this warning message: "Warning message:
>> In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,? :
>> ?? Model failed to converge with max|grad| = 0.00432818 (tol = 0.001,
>> component 1)"
>> I don?t know what is that means, could you help me, please!!
>> My datatable is attached...
>>
>> Cheers, Anah?
>
> (a) Since the function you invoke is glm() this would appear to be
> off-topic for r-sig-mixed-models.? OTOH your formula does indeed seem to
> involve random effects.? Did you *really* call glm()?? Or did you
> actually call glmer()?? If so you, you should be ashamed of yourself for
> such sloppiness in posing your question.? People are providing help out
> of the goodness of their hearts; don't impose on their good nature by
> expecting them to be telepathic.
Rolf, can you tone it down slightly? I agree that the OP could be more
careful, but "you should be ashamed of yourself" seems way too strong.
> (b) Assuming that you really did call glmer() --- my impression is that
> such warnings are usually false positives and may usually (???) be
> safely (???) ignored.? However I'm no expert; you should perhaps wait
> for confirmation of this from the more knowledgeable.
>
> (c) Your "datatable" was *NOT* attached.? Most attachments get stripped
> by the system (for security reasons).? There are exceptions.? *READ* the
> posting guide, which you appear not to have done.
I did get the data from a previous interchange (Anah?, can you post
the data set somewhere publicly accessible? CSV is strongly preferred
to XLSX ...).
The bottom line here is that your baseline category has only a single
'Cuenta' value in it and only two unique 'carga' values, leading to
extreme estimates - this is essentially the analogue of 'complete
separation' in the logistic regression, and has the same solutions
(regularize somehow if you want sensible answers).
cheers
Ben Bolker
Cuenta
categ.asoc 1 2 3 4 5 6 7 8 9
highly 10 0 0 0 0 0 0 0 0
isolated 78 20 8 4 1 0 1 0 0
moderately 58 0 1 0 0 0 0 0 0
poorly 120 47 24 16 12 9 0 1 1
round(coef(summary(M.4)),3)
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.852 1.264 -2.257 0.024
carga 8.029 13.626 0.589 0.556
categ.asocisolated 1.776 1.255 1.415 0.157
categ.asocmoderately 1.299 1.256 1.034 0.301
categ.asocpoorly 1.929 1.251 1.542 0.123
carga:categ.asocisolated -9.548 13.612 -0.701 0.483
carga:categ.asocmoderately -9.418 13.614 -0.692 0.489
carga:categ.asocpoorly -9.006 13.615 -0.661 0.508
>
> cheers,
>
> Rolf Turner
>