[R-sig-ME] Overdispersion lme4 binomial
Chris Mcowen
cm744 at st-andrews.ac.uk
Sun Aug 1 17:31:56 CEST 2010
Hi Jarrord,
Thanks very much for this - i am relatively new to modelling! I am trying to check the goodness of fit of my model before i use it as a predictive model.
I plotted the residuals(see attached) and they looked odd, so i was a little unsure why and thought i would run through a few possibilities. Using a binary response variable what post model checks are appropriate?
Thanks
> plot(resid(model1))
Chris
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On 1 Aug 2010, at 16:11, Jarrod Hadfield wrote:
Dear Chris,
Over-dispersion does not occur with a binary response variable so you don't need to test for it.
This does not mean that between-datum heterogeneity in the probability of success is absent, only that it cannot be observed. For example, take 1000 random draws from a binomial distribution with constant probability (0.5):
table(rbinom(1000, 1, 0.5))
and compare the frequency of outcomes with a 1000 draws from 1000 binomial distributions with different probabilities of success (but with mean = 0.5)
table(rbinom(1000, 1, runif(1000)))
The data look the same, and so the between-datum heterogeneity (residual variance if you like) although it may exist cannot be estimated from the data.
Cheers,
Jarrod
Quoting Chris Mcowen <cm744 at st-andrews.ac.uk>:
> Dear List,
>
> I am wanting to test for overdispersion in my model and am unsure how for my specific case.
>
> I have 2 random factors, 7 fixed factors that have multiple levels and are categorical and then i have a binary response (True or False).
>
> model1 <- lmer(threattf~1+(1|order/family) + geophyte + seasonality + pollendispersal + breedingsystem*fruit + habit + lifeform + woodyness, family=binomial)
>
> I would be very grateful if somebody could point me in the right direction for testing for overdispersion under such scenarios?
>
> Please see part of the output below -
>
> Thanks for any help, and if more data is required feel free to ask.
>
> Chris
>
> Generalized linear mixed model fit by the Laplace approximation
> Formula: threattf ~ 1 + (1 | order/family) + geophyte + seasonality + pollendispersal + breedingsystem * fruit + habit + lifeform + woodyness
> AIC BIC logLik deviance
> 1562 1649 -764.2 1528
> Random effects:
> Groups Name Variance Std.Dev.
> family:order (Intercept) 0.26932 0.51896
> order (Intercept) 0.00000 0.00000
> Number of obs: 1242, groups: family:order, 43; order, 9
>
> Fixed effects:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -0.10413 0.98004 -0.106 0.91538
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>
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