[R-sig-ME] warnings when using binomial models and offset (log(x))

[Ben Bolker]

  Have you looked at the ?convergence help page?

  By the way, what is the purpose of the +2 in your offset term?  Are
you still centering your offset?

On 2018-11-26 8:47 a.m., Joana Martelo wrote:
> Thanks for your help!
> 
> However, I still get the warnings when using offset(log(density)
> 
> 
>> Model1<-glmer(capture~length+offset(log(density+2))+(1|fish.id.c),family=binomial,data=cap)
> 
> 
> Warning messages:
> 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
>   Model failed to converge with max|grad| = 0.258231 (tol = 0.001, component 1)
> 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
>   Model is nearly unidentifiable: very large eigenvalue
>  - Rescale variables?
> 
> 
> Any suggestion?
> 
> Thanks
> Joana
> 
> 
> 
> -----Mensagem original-----
> De: Mollie Brooks [mailto:mollieebrooks using gmail.com] 
> Enviada: segunda-feira, 26 de Novembro de 2018 12:36
> Para: Joana Martelo
> Cc: R SIG Mixed Models
> Assunto: Re: [R-sig-ME] warnings when using binomial models and offset - NaNs
> 
> If you’re using the scale() function to standardize your density values, you could use the argument, center=FALSE, to avoid subtracting the mean and thus avoid negative densities. 
> 
> cheers,
> Mollie
> 
>> On 26Nov 2018, at 13:33, Joana Martelo <joanamartelo using gmail.com> wrote:
>>
>> Thanks for your email!
>>
>> Warnings' problem is solved, however, when I use log(density) or 
>> log(density+1) I got NaNs because density has negative numbers. 
>> Density is 2,4,6 which standardized gives -1.793073717, -0.450015136, 
>> 0.893043446. So, log(-1.793073717+1)= NaN
>>
>> Any suggestions?
>>
>> Many thanks!
>> Joana
>>
>>
>> -----Mensagem original-----
>> De: R-sig-mixed-models 
>> [mailto:r-sig-mixed-models-bounces using r-project.org] Em nome de Ben 
>> Bolker
>> Enviada: sexta-feira, 23 de Novembro de 2018 21:54
>> Para: r-sig-mixed-models using r-project.org
>> Assunto: Re: [R-sig-ME] warnings when using binomial models and offset
>>
>>
>>  This is a pretty common error, which I've now added to the GLMM FAQ.
>> You should be using log(density), not density, as your offset term; if you use density, then you end up specifying that your capture counts are proportional to exp(density), which is often a ridiculously huge number.
>>
>> cheers
>>   Ben Bolker
>>
>> On 2018-11-23 12:26 p.m., Joana Martelo wrote:
>>> Hello everyone
>>>
>>>
>>>
>>> I'm trying to model fish capture success using length, velocity and 
>>> group composition as explanatory variables, density as an offset 
>>> variable, and fish.id. as random effect. I'm getting the follow warnings:
>>>
>>>
>>>
>>> Model1<-glmer(capture~length+offset(density)+(1|fish.id),family=binom
>>> i
>>> al,dat
>>> a=cap)
>>>
>>>
>>>
>>> Warning messages:
>>>
>>> 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
>>>
>>>  Model failed to converge with max|grad| = 0.260123 (tol = 0.001, 
>>> component
>>> 1)
>>>
>>> 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
>>>
>>>  Model is nearly unidentifiable: very large eigenvalue
>>>
>>> - Rescale variables?
>>>
>>>
>>>
>>>
>>>
>>> -          I only get the warnings when I use length and group composition,
>>> not with velocity.
>>>
>>> -          I don't get any warning if I don't use the offset.
>>>
>>>
>>>
>>> I've tried:
>>>
>>> Model1<-glmer(capture~length+offset(log(density))+(1|fish.id.c),famil
>>> y
>>> =binom
>>> ial(link="cloglog"),data=cap)
>>>
>>>
>>>
>>> But still get the warning.
>>>
>>>
>>>
>>> Any ideas of what might be the problem?
>>>
>>>
>>>
>>> Many thanks!
>>>
>>>
>>>
>>>
>>>
>>> Joana Martelo
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> Melhores cumprimentos,
>>>
>>>
>>>
>>> Joana Martins
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> 	[[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models using r-project.org mailing list 
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>
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