[R-sig-ME] Trouble looping model using glmmTMB

[Viraj Torsekar]

Thanks so much for the suggestion Tom. Yes I was analysing calling effort
for males, and movement for both males and females in that manner. But I
was running separate models for each. I'll have a look at these conditional
two-part models.

Viraj

On 9 April 2018 at 15:50, Houslay, Tom <T.Houslay at exeter.ac.uk> wrote:

> Hi Viraj,
>
>
> This isn't a direct answer to your question so you can feel extremely free
> to ignore it(!), but your question reminded me of an approach I took to
> modelling calling effort in crickets using zero-altered poisson models in
> Jarrod's MCMCglmm package. In that case I had a few more predictor
> variables, but it meant the question could be phrased as two parts: what
> factors affect whether a male called or not, and - given he did call - what
> factors affect how much time he spent calling? It seems that that might be
> another option for you to model the movement with your crickets, although
> obviously it depends whether you think that would give you anything more
> valuable than your current approach (eg, is the decision to move something
> worth modelling separately from how far the cricket travels).
>
>
> Anyway - my paper including this analysis is at http://doi.wiley.com/10.
> 1111/1365-2435.12766 in case it's of any interest.
>
>
> Cheers
>
>
> Tom
>
> ----
>
> Message: 1
> Date: Sun, 8 Apr 2018 19:57:53 +0530
> From: Viraj Torsekar <viraj.torsekar at gmail.com>
> To: r-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] Trouble looping model using glmmTMB
> Message-ID:
>         <CAOJBL42qdVortWMi2xVdqv=4MHUT8zbiOPbU3Y+UxBSjkiAY0Q at mail.
> gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
>
> Hello all,
>
> I'm trying to find out if distance moved by crickets is a function of
> predation risk. My response variable is 'distance moved' and the predictor
> is probability of spatial proximity with predator, ranging from 0 to 1. The
> response variable is zero-inflated (about 77% values are zeroes) and its
> variance is far higher than its mean. Hence, I tried running zero-inflated
> negative binomial mixed models using glmmADMB, which failed (mixed because
> I have multiple values per individual). Following was the error I kept
> encountering: "function maximizer failed" (attaching a text file with
> details of this model by keeping debug=TRUE).
>
> Hence, I shifted to glmmTMB (version: 0.2.0), on Dr. Bolker's advice, and
> it worked! But the problem is, when I try bootstrapping the model using to
> obtain confidence intervals, I keep getting the following error after
> varying number of runs: 'Error in optimHess(par.fixed, obj$fn, obj$gr):
> gradient in optim evaluated to length 1 not 5'. This non-parametric
> bootstrapping routine involves for loops in which the model is run using
> bootstrapped groups (belonging to the grouping variable; individual.id in
> my case) and the model coefficients thus obtained constitute the confidence
> intervals. I've tried running 10,000 iterations, but the error pops up
> within 10 to 100 runs.
>
> Does anyone have suggestions regarding what can be changed?
>
> Details of the model run singly and not in the loop:
>
>  Family: nbinom2  ( log )
> Formula:          movement.whole ~ poc + (1 | female.id)
> Zero inflation:                  ~1
> Data: incrisk_females_comm
>
>      AIC      BIC   logLik deviance df.resid
>   1725.1   1745.9   -857.5   1715.1      474
>
> Random effects:
>
> Conditional model:
>  Groups    Name        Variance Std.Dev.
>  female.id (Intercept) 0.07924  0.2815
> Number of obs: 479, groups:  female.id, 110
>
> Overdispersion parameter for nbinom2 family (): 1.59
>
> Conditional model:
>             Estimate Std. Error z value Pr(>|z|)
> (Intercept)  4.66384    0.14161   32.93   <2e-16 ***
> poc         -0.08815    0.20978   -0.42    0.674
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Zero-inflation model:
>             Estimate Std. Error z value Pr(>|z|)
> (Intercept)   1.2442     0.1098   11.34   <2e-16 ***
>
> Please do mention if you need further details. Thank you in advance.
>
> Viraj Torsekar,
> PhD Candidate,
> Centre for Ecological Sciences,
> Indian Institute of Science
>
>         [[alternative HTML version deleted]]
>
>
>
> ------------------------------
>
> End of R-sig-mixed-models Digest, Vol 136, Issue 19
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