model check for negative binomial model
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 at 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 at inbo.be <mailto:thierry.onkelinx at 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 at inbo.be <mailto:thierry.onkelinx at 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 at gmail.com <mailto:bielli.alessandra at 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 at gmail.com
<mailto:bbolker at 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
> >
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org
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>
> _______________________________________________
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