[R-sig-ME] mixed effects model glmer

[M West]

Ah. that worked - no idea why that happened.
Thanks so much! - I'll know what it looks like if that ever happens again.

M.

On Thu, Sep 24, 2015 at 3:44 AM, Thierry Onkelinx <thierry.onkelinx at inbo.be>
wrote:

> 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
> Belgium
>
> 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
> with
> >> 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())
> (or
> > something like that) to see if you should worry about autoregressive
> structure.
> >
> >   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
> variance
> > 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
> value
> >>    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
> 1.49e-10
> >> ***
> >> disease prevalence    0.064935065     -0.36344   0.30810  -1.180
> 0.238157
> >>
> >> disease prevalence    0.078507945    -2.57479    0.46537  -5.533
> 3.15e-08
> >> ***
> >> disease prevalence    0.120039255    -3.30998    0.71915  -4.603
> 4.17e-06
> >> ***
> >> disease prevalence    0.182623706     -0.14362   0.19899  -0.722
> 0.470438
> >>
> >>
> >
> > _______________________________________________
> > R-sig-mixed-models at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

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