[R-sig-ME] Could the random effect at the level of each observation be a trap?
bbolker at gmail.com
Sat Dec 11 19:27:56 CET 2010
On 10-12-10 09:39 PM, Billy wrote:
> Hi all!
> I'm a relatively newbie ecologist student getting adventures at the
> mixed models world and facing some trouble to interpret random
> effects. I hope someone could help me.
> Quickly, I'm constructing different models using glmer() to discover
> which factors could influence females' reproductive decisions. I have
> sampled several males and classified them as successful or
> unsuccessful. Therefore, I'm modelling logistic regressions with more
> than one fixed variable and random variables.
> I have sampled individuals monthly and, sometimes, the same individual
> (MaleID) was sampled more than once, in different status. Then, I used
> "MaleID" as a random variable.
> Well, I built a bunch of models considering only MaleID as the random
> variable as:
> m1 <- glmer(y ~ 1 + (1|MaleID), family=binomial)
> m2 <- glmer(y ~ x + (1|MaleID), family=binomial)
> m3 <- glmer(y ~ z + (1|MaleID), family=binomial)
> Moreover, in several posts here I've read about count data show high
> overdispersion, even using family=binomial for the error. One
> recurrent solution suggested is create a vector to each observation
> resid <- as.factor(1:dim(data))
> Then, I built models considering this random variable too, trying to
> understand that, as following
> m4 <- glmer(y ~ 1 + (1|resid), family=binomial)
> m5 <- glmer(y ~ x + (1|resid), family=binomial)
> m6 <- glmer(y ~ w + (1|resid), family=binomial)
> m7 <- glmer(y ~ 1 + (1|MaleID:resid ), family=binomial)
> m8 <- glmer(y ~ x + (1|MaleID:resid ), family=binomial)
> m9 <- glmer(y ~ z + (1|MaleID:resid ), family=binomial)
> Using a model selection approach and looking for the deviance and the
> AIC values, I observed that the correspondent models from the second
> block (m4, m5 and m6 in the example above) and the third block (m7, m8
> and m9) showed the same values. Is that due to the "resid" random
I think you want
~ [fixed predictor(s)] + (1|MaleID)+(1|resid)
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