[R-sig-ME] warnings when using binomial models and offset - NaNs
Ben Bolker
bbolker @ending from gm@il@com
Mon Nov 26 15:01:11 CET 2018
More generally: I would ask whether it makes sense to scale the
density at all; if your density is measured in sensible areal units
(e.g. individuals/hectare), then leaving it as-is will mean that your
other parameters will be in units of their effects on
capture/(individuals/hectare). Maybe you've been standardizing all of
your predictors according to the (generally wise) advice that
standardizing makes parameters more comparable/interpretable -- but
offsets are an exception to this advice ...
On 2018-11-26 7:35 a.m., Mollie Brooks wrote:
> 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=binomi
>>> 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),family
>>> =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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