[R-sig-ME] Fwd: model check for negative binomial model
Alessandra Bielli
b|e|||@@|e@@@ndr@ @end|ng |rom gm@||@com
Tue Feb 18 01:01:48 CET 2020
Dear Ben
I am trying to make a prediction for the combination of species (L or G)
and treatment (control/experiment).
I am still confused about the prediction values. I would like to present
results as a success rate for a nest, to say that treatment
increases/decreases success by ...%. But the value I have is the
probability that 1 egg in the nest succeeds, correct? I am not sure how to
use these predictions.
Thanks for your help!
Alessandra
On Mon, Feb 17, 2020 at 2:15 PM Ben Bolker <bbolker using gmail.com> wrote:
>
> 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>
> >
> >
> ///////////////////////////////////////////////////////////////////////////////////////////
> > 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
> >
> ///////////////////////////////////////////////////////////////////////////////////////////
> >
> > <https://www.inbo.be>
> >
> >
> > 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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