[R-sig-ME] [FORGED] warning error question

[John Maindonald]

What may be most useful is advice to, wherever possible
(i.e., unless one has an equipment setup that does not allow it),
copy the code as executed from the command line or script into
the email message.  Newies may take time to learn to make
such actions routine.

John Maindonald             email: john.maindonald using anu.edu.au<mailto:john.maindonald using anu.edu.au>


On 24/03/2020, at 13:50, Ben Bolker <bbolker using gmail.com<mailto:bbolker using gmail.com>> wrote:



On 2020-03-23 6:33 p.m., Rolf Turner wrote:

On 24/03/20 3:41 am, Anahí Fernández 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, Anahí

(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 (Anahí, 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


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