[R-sig-ME] Fwd: model check for negative binomial model
Ben Bolker
bbo|ker @end|ng |rom gm@||@com
Mon Feb 17 21:15:52 CET 2020
That's correct. There are some delicate issues about prediction:
* do you want to use the original (potentially unbalanced) data for
prediction? (That's what you're doing here).
* or, do you want to make predictions for a "typical" nest and week
combination, in which case you would use
pframe <- with(your_data,
expand.grid(Relocation..Y.N.=unique(Relocation..Y.N.),
SP=unique(SP))
predict(m.unhatched,type="response",re.form=NA,newdata=pframe))
You could also use expand.grid() to generate a balanced design (i.e.
all combinations of weeks and nests), which would give yet another answer.
There are a lot of packages designed for doing these kinds of
post-fitting manipulations (e.g. 'margins', 'emmeans'), you might find
them useful ...
On 2020-02-17 1:48 p.m., Alessandra Bielli wrote:
> Dear Thierry
>
> Thanks for your reply.
>
> I read a bit about the prediction for a binomial model with
> success/failures and I have a couple of questions.
>
> If I use the predict function with the model you recommended, I obtain
> log.odds or probabilities if I use "type=response":
>
> tapply(predict(m.unhatched,type="response"),list(main$SP,main$Relocation..Y.N.),mean)
> N Y
> G 0.7314196 0.6414554
> L 0.6983576 0.6003087
>
> Are these probabilities of success (i.e. hatched) in one nest?
>
> Thanks,
>
> Alessandra
>
> On Mon, Feb 17, 2020 at 7:18 AM Thierry Onkelinx
> <thierry.onkelinx using inbo.be <mailto:thierry.onkelinx using inbo.be>> wrote:
>
> Dear Alessandra,
>
> Since you have both the number hatched and the total clutch size you
> can calculate the number of successes and failures. That is
> sufficient for a binomial distribution.
>
> glmer(cbind(Hatched, Unhatched) ~ Relocation..Y.N. + SP + (1 |
> Beach_ID) + (1 | Week), family = binmial)
>
> A negative binomial or Poisson allow predictions larger than the
> offset. Which is nonsense given that the number hatched cannot
> surpass the total clutch size.
>
> Best regards,
>
> ir. Thierry Onkelinx
> Statisticus / Statistician
>
> Vlaamse Overheid / Government of Flanders
> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR
> NATURE AND FOREST
> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
> thierry.onkelinx using inbo.be <mailto:thierry.onkelinx using inbo.be>
> Havenlaan 88 bus 73, 1000 Brussel
> www.inbo.be <http://www.inbo.be>
>
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>
> Op wo 12 feb. 2020 om 18:42 schreef Alessandra Bielli
> <bielli.alessandra using gmail.com <mailto:bielli.alessandra using gmail.com>>:
>
> Dear Ben
>
> Thanks for your quick response.
>
> Yes, emergence success is usually between 60 and 80% or higher.
> I am not sure how to use a binomial, if my data are counts?
>
> Can you explain why the approximation doesn't work well if
> success gets
> much above 50%? Does it make sense, then, to have "unhatched" as
> dependent
> variable, so that I predict mortality (usually below 50%) using
> a nb with
> offset(log(total clutch)) ?
>
> > summary(m.emerged)
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) ['glmerMod']
> Family: Negative Binomial(2.2104) ( log )
> Formula: Unhatched ~ Relocation..Y.N. + SP +
> offset(log(Total_Clutch)) +
> (1 | Beach_ID) + (1 | Week)
> Data: main
>
> AIC BIC logLik deviance df.resid
> 5439.4 5466.0 -2713.7 5427.4 614
>
> Scaled residuals:
> Min 1Q Median 3Q Max
> -1.4383 -0.7242 -0.2287 0.4866 4.0531
>
> Random effects:
> Groups Name Variance Std.Dev.
> Week (Intercept) 0.003092 0.0556
> Beach_ID (Intercept) 0.025894 0.1609
> Number of obs: 620, groups: Week, 31; Beach_ID, 8
>
> Fixed effects:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -1.38864 0.08227 -16.879 < 2e-16 ***
> Relocation..Y.N.Y 0.32105 0.09152 3.508 0.000452 ***
> SPL 0.22218 0.08793 2.527 0.011508 *
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Correlation of Fixed Effects:
> (Intr) R..Y.N
> Rlct..Y.N.Y -0.143
> SPL -0.540 -0.038
>
> Thanks,
>
> Alessandra
>
> On Tue, Feb 11, 2020 at 7:29 PM Ben Bolker <bbolker using gmail.com
> <mailto:bbolker using gmail.com>> wrote:
>
> >
> > Short answer: if emergence success gets much above 50%, then the
> > approximation you're making (Poisson + offset for binomial, or
> NB +
> > offset for negative binomial) doesn't work well. You might try a
> > beta-binomial (with glmmTMB) or a binomial + an
> observation-level random
> > effect.
> >
> > (On the other hand, your intercept is -0.3, which
> corresponds to a
> > baseline emergence of 0.42 - not *very* high (but some beaches
> and years
> > will be well above that ...)
> >
> > Beyond that, are there any obvious patterns of mis-fit in the
> > predicted values ... ?
> >
> > On 2020-02-11 8:09 p.m., Alessandra Bielli wrote:
> > > Dear list
> > >
> > > I am fitting a poisson model to estimate the effect of a
> treatment on
> > > emergence success of hatchlings. To estimate emergence
> success, I use
> > > number of emerged and an offset(log(total clutch).
> > >
> > > However, overdispersion was detected:
> > >
> > >> overdisp_fun(m.emerged) #overdispersion detected
> > >
> > > chisq ratio rdf p
> > > 3490.300836 5.684529 614.000000 0.000000
> > >
> > > Therefore, I switched to a negative binomial. I know
> overdispersion is
> > not
> > > relevant for nb models, but the model plots don't look too
> good. I also
> > > tried to fit a poisson model with OLRE, but still the plots
> don't look
> > > good.
> > > How do I know if my model is good enough, and what can I do
> to improve
> > it?
> > >
> > >> summary(m.emerged)
> > > Generalized linear mixed model fit by maximum likelihood
> (Laplace
> > > Approximation) ['glmerMod']
> > > Family: Negative Binomial(7.604) ( log )
> > > Formula: Hatched ~ Relocation..Y.N. + SP +
> offset(log(Total_Clutch)) + (1
> > > |Beach_ID) + (1 | Year)
> > > Data: main
> > >
> > > AIC BIC logLik deviance df.resid
> > > 6015.6 6042.2 -3001.8 6003.6 614
> > >
> > > Scaled residuals:
> > > Min 1Q Median 3Q Max
> > > -2.6427 -0.3790 0.1790 0.5242 1.6583
> > >
> > > Random effects:
> > > Groups Name Variance Std.Dev.
> > > Beach_ID (Intercept) 0.004438 0.06662
> > > Year (Intercept) 0.001640 0.04050
> > > Number of obs: 620, groups: Beach_ID, 8; Year, 5
> > >
> > > Fixed effects:
> > > Estimate Std. Error z value Pr(>|z|)
> > > (Intercept) -0.29915 0.04055 -7.377 1.62e-13 ***
> > > Relocation..Y.N.Y -0.16402 0.05052 -3.247 0.00117 **
> > > SPL -0.08311 0.04365 -1.904 0.05689 .
> > > ---
> > > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> > >
> > > Correlation of Fixed Effects:
> > > (Intr) R..Y.N
> > > Rlct..Y.N.Y -0.114
> > > SPL -0.497 -0.054
> > >
> > >
> > > Thanks for your help,
> > >
> > > Alessandra
> > >
> > >
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