[R-sig-ME] Convergence Problems with glmer.nb model
Aoibheann Gaughran
gaughra at tcd.ie
Mon Apr 25 15:44:28 CEST 2016
Hi Thierry,
Here is the dropbox link to the data -
https://www.dropbox.com/s/ne5d4zp2gncwylm/foraging%20subset.csv?dl=0
I had changed the field area units from meter square to hectures already,
so that *should *be okay. There is one exceedingly large "field" which is
actually a large area of forestry. Perhaps this is throwing the scaling
off.
Can you confirm that this is how I specify the observation level random
effect:
dframe1$obs <- factor(seq(nrow(dframe1))) #modified from
https://rpubs.com/bbolker/glmmchapter
which is included in the model as
+(1|obs)
I'll try your suggestions and see how I get on.
Many thanks,
Aoibheann
On 25 April 2016 at 13:40, Thierry Onkelinx <thierry.onkelinx at inbo.be>
wrote:
> Dear Aoibheann,
>
> Two general suggestions on the design. 1) A random effect of field seems
> relevant too. 2) Have the units of origarea in a relevant scale. You are
> modelling the number of visits per unit of origarea. Then the number per
> hectare seems more relevant to me than the number per square meter.
>
> Then try the most simple Poisson model to see it that converge. glmer(field_count
> ~ (1| animal) + (1|field) + offset(log(origarea)), family = poisson)
>
> If that works, then you could try the negative binomial distribution or
> adding an observation level random effect.
>
> The mailing list strips most attachments. So you need to put them on a
> website or post a dropbox or google drive link.
>
> Best regards,
>
>
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
> Forest
> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> Belgium
>
> To call in the statistician after the experiment is done may be no more
> than asking him to perform a post-mortem examination: he may be able to say
> what the experiment died of. ~ Sir Ronald Aylmer Fisher
> The plural of anecdote is not data. ~ Roger Brinner
> The combination of some data and an aching desire for an answer does not
> ensure that a reasonable answer can be extracted from a given body of data.
> ~ John Tukey
>
> 2016-04-25 14:08 GMT+02:00 Aoibheann Gaughran <gaughra at tcd.ie>:
>
>> On 25 April 2016 at 13:00, Aoibheann Gaughran <gaughra at tcd.ie> wrote:
>>
>> > Good morning,
>> >
>> > First time posting so I hope I am including all of the relevant
>> > information.
>> >
>> > I am attempting to analyse the foraging behaviour of a animal in an
>> > agricultural landscape. The objective is to identify the factors
>> (habitat
>> > type, environmental variables and animal-specific variables) that best
>> > predict foraging site preference. Some fields are preferred while others
>> > are avoided.
>> >
>> > The response variable is count data - the number of times a given animal
>> > was in a given field in a given month. An animal's home range varies
>> from
>> > month to month, so the area available to it and the fields that fall
>> within
>> > its home range change somewhat every month. The count data shows an
>> > overdispersed, negative binomial distribution, and is zero inflated as
>> > fields that fell within the home range where the animal had *not
>> *foraged
>> > in that month are also included in the dataset. The individual animal is
>> > specified as a random variable to account for pseudoreplication.
>> >
>> > It should be noted that at the moment I am attempting to run a the model
>> > on a subset of the data (n=671) as I had attempted to run the model on
>> the
>> > full dataset (n=62,000) but three days later the model (which included
>> > interaction terms at this point) had still failed to run, and when
>> stopped,
>> > R gave me a multitude of convergence warning messages e.g.
>> >
>> > 13: In (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int
>> (Inf,
>> > ... :
>> > failure to converge in 10000 evaluations
>> >
>> > Simpler iterations of the model, with fewer explanatory terms, and no
>> > interaction terms, also gave me convergence and some scaling warnings,
>> > which I sought to address using:
>> >
>> > control=glmerControl(optCtrl=list(maxfun=20000)
>> >
>> > and by scaling the numeric variables age, slope and aspect as follows:-
>> >
>> > dframe1$agescale <- scale(dframe1$age, center = TRUE, scale = FALSE)
>> > dframe1$slopescale <- scale(dframe1$slope, center = TRUE, scale = FALSE)
>> > dframe1$aspectscale <- scale(dframe1$aspect, center = TRUE, scale =
>> FALSE)
>> >
>> > Currently, the model looks like this:
>> >
>> > > model1 <- glmer.nb(field_count ~ habitat + + sex+
>> + agescale+ #+ mon+
>> + soil+ + slopescale+ + aspectscale+
>> + offset(log(origarea)) #take into account field size +
>> +(1|animal),+
>> control=glmerControl(optCtrl=list(maxfun=20000)),+ data
>> = dframe1)
>> >
>> > There were 24 warnings (use warnings() to see them)
>> > > warnings()Warning messages:
>> > 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl =
>> control$checkConv, ... :
>> > Model is nearly unidentifiable: very large eigenvalue
>> > - Rescale variables?;Model is nearly unidentifiable: large eigenvalue
>> ratio
>> > - Rescale variables?
>> > 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl =
>> control$checkConv, ... :
>> > Model failed to converge with max|grad| = 0.0134799 (tol = 0.001,
>> component 1)
>> > 3: In checkConv(attr(opt, "derivs"), opt$par, ctrl =
>> control$checkConv, ... :
>> > Model failed to converge with max|grad| = 0.148644 (tol = 0.001,
>> component 1)
>> > 4: In checkConv(attr(opt, "derivs"), opt$par, ctrl =
>> control$checkConv, ... :
>> > Model is nearly unidentifiable: large eigenvalue ratio
>> > - Rescale variables?
>> >
>> > etc.
>> >
>> > So the model still fails to converge despite rescaling and altering the
>> > number of iterations. I had also received the following error in
>> relation
>> > to month (in the reduced dataset there are only *four *months), so Ive
>>
>> > had to exclude it for the time being. I am not sure why I am getting
>> this
>> > error since the factor has four levels.
>> >
>> > Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
>> > contrasts can be applied only to factors with 2 or more levels
>> >
>> > I do eventually want to include interaction terms as previous analysis
>> on
>> > ranging behaviour suggests there is an interaction between age and sex.
>> >
>> > Summary of dataset attached. Also attached is the .csv file containing
>> > the reduced dataset.
>> >
>> > I have read various suggestions online and have come across the
>> following
>> > worrying line "It's perfectly possible that your data is insufficient to
>> > support the complexity of the model or the model is incorrectly
>> constructed
>> > for the design of the study".
>> >
>> > I would greatly appreciate any help you could give me with understanding
>> > and solving the problems I am encountering with my model.
>> >
>> > Kind regards,
>> >
>> > --
>> > Aoibheann Gaughran
>> >
>> > Behavioural and Evolutionary Ecology Research Group
>> > Zoology Building
>> > School of Natural Sciences
>> > Trinity College Dublin
>> > Dublin 2
>> > Ireland
>> > Phone: +353 (86) 3812615
>> >
>>
>>
>>
>> --
>> Aoibheann Gaughran
>>
>> Behavioural and Evolutionary Ecology Research Group
>> Zoology Building
>> School of Natural Sciences
>> Trinity College Dublin
>> Dublin 2
>> Ireland
>> Phone: +353 (86) 3812615
>> _______________________________________________
>> R-sig-mixed-models at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
>
--
Aoibheann Gaughran
Behavioural and Evolutionary Ecology Research Group
Zoology Building
School of Natural Sciences
Trinity College Dublin
Dublin 2
Ireland
Phone: +353 (86) 3812615
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