[R-sig-ME] mixed effects model glmer
westm490 at gmail.com
Thu Sep 24 18:31:34 CEST 2015
Ah. that worked - no idea why that happened.
Thanks so much! - I'll know what it looks like if that ever happens again.
On Thu, Sep 24, 2015 at 3:44 AM, Thierry Onkelinx <thierry.onkelinx at inbo.be>
> Adding a random effect is equivalent to a compound symmetry
> correlation structure. Since you have only 4 years, it would be too
> bad compared to an AR1 correlation structure.
> If you really need correlated random effects, then you can have a look
> at the INLA package. Not on CRAN but on www.r-inla.org.
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature
> and Forest
> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> 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
> 2015-09-23 22:41 GMT+02:00 Ben Bolker <bbolker at gmail.com>:
> > On Wed, Sep 23, 2015 at 2:36 PM, M West <westm490 at gmail.com> wrote:
> >> I am trying to fit a mixed effects model with repeated measures data.
> >> Data are:
> >> y variable = percentage (# females/total)
> >> x variable = percentage
> >> measured across multiple sites for 4 years.
> >> here's the model:
> >> y <- cbind(total females, (Total - total females)))
> >> mod1 <- with(data, glmer(y ~ disease prevalence + (1|Site) + (1|Year),
> >> family = binomial, data = data1))
> > Just to be clear, disease prevalence is a number in [0,1]?
> >> 1) This model runs, but the summary(mod1) just generates a series of the
> >> following....which doesn't make any sense so something must be wrong
> >> the model specification...I'm just not sure what.
> >> 2) Also, what is the default AR correlation on these models (i.e., do I
> >> need to specify it or is the temporal psuedoreplication taken care of)?
> > AR models are not currently easy in lme4. My suggestion (=hack) would
> > be to get the residuals and use nlme::gls(resid~1,correlation=corAR1())
> > something like that) to see if you should worry about autoregressive
> > Four years is not very many, so you might need to treat Year as a
> > fixed effect (e.g. I would consider that option if the random effects
> > is estimated as zero)
> > How many sites? How many total observations?
> > I have to admit that I'm stumped by your apparent model output (i.e.
> > that there are multiple parameters for disease prevalence when there
> > should only be one)
> > Perhaps you could send the results of summary(data1) and/or
> > str(data1) and summary() of your whole model?
> >> 3) Finally, do you suggest another form of the model that's better etc.?
> >> Fixed effects:
> >> Estimate Std. Error z
> >> Pr(>|z|)
> >> (Intercept) -1.60267 0.11618 -13.794 <
> >> 2e-16 ***
> >> disease prevalence -0.40212 0.15557 -2.585
> 0.009745 **
> >> disease prevalence 0.035088231 -1.46452 0.22860 -6.406
> >> ***
> >> disease prevalence 0.064935065 -0.36344 0.30810 -1.180
> >> disease prevalence 0.078507945 -2.57479 0.46537 -5.533
> >> ***
> >> disease prevalence 0.120039255 -3.30998 0.71915 -4.603
> >> ***
> >> disease prevalence 0.182623706 -0.14362 0.19899 -0.722
> > _______________________________________________
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> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
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