[R-sig-ME] glmmADMB errors

Fri Oct 20 14:10:19 CEST 2017

Sorry, I didn't see in your email that you had tried the Gamma distribution
in glmmADMB.

The cause of the error may have been the nesting of the random effect. In
my opinion, the nesting notation causes confusion quite often and it's best
to just give each of the 8 locations (i.e. RE levels) a unique name. If, on
the other hand, there are only 4 unique locations and both treatments are
in the same location, then location should be a fixed effect because 4
levels is too few for a random effect.

If you're fitting a zero-inflated model, you might have better luck with
convergence if you allow the zero-inflation to vary with parameters.

I would try these models if there are 8 uniquely coded locations
m1 <- glmmTMB(Biomass ~ Protection * Exposure +(1|Location),
data=dataGLMMADMB, family=Gamma(link="log"))

m2 <- glmmTMB(Biomass ~ Protection * Exposure +(1|Location),
zi =~ Protection * Exposure,
data=dataGLMMADMB, family=Gamma(link="log"))

On Fri, Oct 20, 2017 at 1:51 PM, Mollie Brooks <mollieebrooks at gmail.com>
wrote:

> Hi Andreu,
>
> A zero-inflated Poisson distribution is not appropriate because biomass is
> not count data. I would recommend checking what distribution other
> researchers in your field are using. Maybe you want to first model zero vs
> non-zero and then model the non-zero biomasses separately. The log of
> non-zero biomasses could be modeled with a normal distribution. Or on the
> natural scale, they could be Gamma or Tweedie. Or maybe a zero-inflated
> continuous positive distribution (e.g. Gamma or Tweedie) makes sense for
> all of the biomasses. These zero-inflated models could be fit in glmmTMB.
>
> cheers,
> Mollie
>
> On Thu, Oct 19, 2017 at 2:03 PM, andreu blanco <andreu.blanco at gmail.com>
> wrote:
>
>>  Dear list members, I am starting with generalized mixed models and I am
>> having some trouble I hope someone could help me with.
>>
>> We are trying to understand the invasiveness of algae inside and outside
>> MPA, to do so our sampling was set with a nested desing:
>>
>> Protected vs nonProtected
>> 4 Locations (protected) vs 4 Locations (nonProtected)
>> Exposed vs Semiexposed at each location
>> 1 transect per sampling point (total 16)
>> 5 quadrants per transect
>>
>> str(dataGLMMADMB)
>> 'data.frame':   80 obs. of  4 variables:
>>  $ Location: Factor w/ 4 levels "Cies1","Cies2",..: 1 1 1 1 1 1 1 1 1 1
>> ...
>>  $ Protection: Factor w/ 2 levels "Control","Protected": 1 1 1 1 1 2 2 2 2
>> 2 ...
>>  $ Exposure: Factor w/ 2 levels "Exposed","Semiexposed": 1 1 1 1 1 1 1 1 1
>> 1 ...
>>  $ Biomass: num  124.8 104.8 139.2 102.6 62.9 ...
>>
>>
>> First I ran it as a Poission distribution (after round the Biomass values)
>> to be able to fit a zeroInflation model:
>> > Model_ADMB_P<-glmmadmb(Biomass~Protection+Exposure+Protectio
>> n:Exposure+(1|
>> Protection/Location),data=GLMMADMB_P, zeroInflation=TRUE,
>> family="Poisson")
>> Parameters were estimated, but standard errors were not: the most likely
>> problem is that the curvature at MLE was zero or negative
>> Error in glmmadmb(Biomass ~ Protection + Exposure + Protection:Exposure +
>> :
>>   The function maximizer failed (couldn't find parameter file)
>> Troubleshooting steps include (1) run with 'save.dir' set and inspect
>> output files; (2) change run parameters: see '?admbControl';(3) re-run
>> with
>> debug=TRUE for more information on failure mode
>> Además: Warning message:
>> comando ejecutado 'C:\WINDOWS\system32\cmd.exe /c glmmadmb -maxfn 500
>> -maxph 5 -noinit -shess' tiene estatus 1
>>
>> Then I though that since my data is continuous I'd better run the model
>> with a gamma family, however, when I do run it with gamma I got the
>> following error:
>> > Model_ADMB_G<-glmmadmb(Biomass~Protection+Exposure+Protectio
>> n:Exposure+(1|
>> Protection/Location),data=GLMMADMB_P, family="gamma")
>>
>> Error in glmmadmb(Biomass ~ Protection + Exposure + Protection:Exposure +
>> :
>>   The function maximizer failed (couldn't find parameter file)
>> Troubleshooting steps include (1) run with 'save.dir' set and inspect
>> output files; (2) change run parameters: see '?admbControl';(3) re-run
>> with
>> debug=TRUE for more information on failure mode
>> Además: Warning message:
>> comando ejecutado 'C:\WINDOWS\system32\cmd.exe /c glmmadmb -maxfn 500
>> -maxph 5 -noinit -shess' tiene estatus 1
>>
>> However, when I run it as a Poisson distribution with zeroInflated values
>> but with no nested design and Location effect either, it ran ok
>> > Model_ADMB_P1<-glmmadmb(Biomass~Protection*Exposure,data=GLMMADMB_P,
>> zeroInflation=TRUE, family="Poisson")
>> > summary(Model_ADMB_P1)
>>
>> Call:
>> glmmadmb(formula = Biomass ~ Protection * Exposure, data = GLMMADMB_P,
>>     family = "Poisson", zeroInflation = TRUE)
>>
>> AIC: 1570.7
>>
>> Coefficients:
>>                                          Estimate Std. Error z value
>> Pr(>|z|)
>> (Intercept)                              4.71e+00   3.18e-02   148.0
>>  <2e-16
>> ProtectionProtected                     -5.53e-01   5.00e-02   -11.1
>>  <2e-16
>> ExposureSemiexposed                     -3.83e+01   2.22e+05     0.0
>> 1
>> ProtectionProtected:ExposureSemiexposed  3.64e+01   2.22e+05     0.0
>> 1
>>
>> (Intercept)                             ***
>> ProtectionProtected                     ***
>> ExposureSemiexposed
>> ProtectionProtected:ExposureSemiexposed
>> ---
>> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>
>> Number of observations: total=80
>> Zero-inflation: 0.30908  (std. err.:  0.071433 )
>>
>> Log-likelihood: -780.37
>> >
>>
>>
>> I can not understat the solutions to these errors, can anyone please help
>> me out?
>> I really appreciate it!
>>
>> Thanks in advance,
>>
>> --
>> Andreu Blanco Cartagena
>>
>>
>>
>> Si no és imprescindible, no imprimeixis aquest e-mail. Estalviar paper
>> ajuda a protegir el medi ambient.
>>
>> Si no es imprescindible, no imprimas este e-mail. Ahorrar papel ayuda a
>> proteger el medio ambiente.
>>
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>>
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>
>
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