[R-sig-ME] mixed effects model glmer
thierry.onkelinx at inbo.be
Thu Sep 24 09:44:00 CEST 2015
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
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
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 op gmail.com>:
> On Wed, Sep 23, 2015 at 2:36 PM, M West <westm490 op 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
>> (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
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