[R-sig-ME] glmmADMB errors
andreu blanco
andreu.blanco at gmail.com
Mon Oct 23 09:12:51 CEST 2017
Thank you both very much.
I hope this will work.
On 20 October 2017 at 17:49, Ben Bolker <bbolker at gmail.com> wrote:
> Following up on this: the proximal problem is that you have specified
> Protection as both a random and a fixed effect, because the nesting
> syntax (1|Protection/Location) expands to
> (1|Protection)+(1|Protection:Location). I agree with Mollie that it's
> generally less confusing to give unique values to the Location
> variable, but if not then I would expect
>
> Biomass~Protection+Exposure+Protection:Exposure+
> (1|Protection:Location)
>
> to work better.
>
>
> On Fri, Oct 20, 2017 at 8:10 AM, Mollie Brooks <mollieebrooks at gmail.com>
> wrote:
> > 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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> >
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>
--
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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