[R-sig-ME] Longitudinal Model
Thierry.ONKELINX at inbo.be
Wed Nov 19 14:13:50 CET 2014
Your model seems reasonable in case of many sites and a continuous response. However, 3 levels is too low to get sensible estimates of the random effect variance (see http://glmm.wikidot.com/faq). Furthermore your response seem to be counts, for which the Gaussian distribution in not correct. You 'll need a Poisson or negative binomial distribution. nlme handles only the Gaussian distribution.
I'm afraid that you will have to go for some more complicated tools like the INLA package or JAGS.
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
+ 32 2 525 02 51
+ 32 54 43 61 85
Thierry.Onkelinx op inbo.be
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Van: r-sig-mixed-models-bounces op r-project.org [mailto:r-sig-mixed-models-bounces op r-project.org] Namens Leonardo Bergamini
Verzonden: woensdag 19 november 2014 13:44
Aan: r-sig-mixed-models op r-project.org
Onderwerp: [R-sig-ME] Longitudinal Model
I would like some help at adjusting a mixed model as follows:
I have collected data on the abundance of caterpillars (NC) on trees at three sites over 48 monthly samplings. I am interested in understanding how the three environmental variables I have for the region (namely mean temperature (Temp) in that month, total precipitation (R) and mean relative humidity (H)) correlate with the number of caterpillars, and whether this relationship is different at the three sites.
So far I've been thinking about a lme model, in order to include the temporal autocorrelation structure, but I'm not sure how to specify the random effects and the error structure. I also would like to test at which temporal lag (same month, last month or two months earlier) the relationship is stronger.
## simulating the data
Any help on this specific problem or pointings towards introductory literature would be great Thanks,
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