Hello statistical gurus and R programming wizards,
I am interested in modeling animal captures as a function of environmental conditions and day of the year. As part of another study, I have counts of captures on ~160 days over three years. On each of these days I have temperature, rainfall, windspeed, relative humidity, etc. Because the data was collected repeatedly from the same 5 plots, I use plot as a random effect.
My understanding is that nlme can easily account for temporal autocorrelation in the residuals but doesn't handle non-Gaussian link functions like lme4 (which can't handle the autocorrelation?). Currently, I think it might work to use the nlme package in R on log(count). So my solution right now would be to run something like:
m1 <- lme(lcount ~ AirT + I(AirT^2) + RainAmt24 + I(RainAmt24^2) + RHpct + windspeed +
sin(2*pi/360*DOY) + cos(2*pi/360*DOY), random = ~1|plot, correlation =
corARMA(p = 1, q = 1, form = ~DOY|plot), data = Data)
where DOY = Day of the Year. There may be more interactions in the final model, but this is my general idea. I could also potentially try to model the variance structure further with something like
weights = v1Pow
I'm not sure if there is a better way to do with with a Poisson mixed model regression or anything? I just found mathematical discussion in Chapter 4 of "Regression Models for Time Series Analysis" by Kedem and Fokianos. It was a bit beyond me at the moment, especially in application (coding it in R). I also saw a MCMC solution in Zuur et al. Mixed Effects Models book (Chp 23) in the BUGS language (using winBUGS or JAG). Is that my best option? Is there an easy MCMC package in R that would handle this? I'm not really familiar with GAMM or GEE techniques but would be willing to explore these possibilities if people thought they'd provide better insight. My main objective is to create a model to predict animal captures given environmental conditions. Secondarily, I would like to explain what the animals a responding to in terms of their activity.
Any thoughts on the best way to proceed (philosophically), how to code this in R, or in BUGS would be appreciated. I'm fairly new to R and BUGS (winBUGS) but am learning. This is also the first time I've ever tried to address temporal autocorrelation.
Thanks, Dan
