Heather Major <heather.major@...> writes: > > A small correction to my original post: the arrival > fixed effect is a number (a count) and ranges between 0 - 52. >
> Hello, I am new to R and working to understand the programming > language and how the different tests work. I've jumped a bit in the > deep-end, as I moved to R because SPSS couldn't handle the model I > wanted to run and I don't have access of SAS (which was, until > recently my go-to for stats). > I have done my best to work through a number of examples, but that > hasn't helped me figure out how to proceed with my analysis. > I am using the glmmADMB package to analyze count data of arrivals at > a seabird colony. > Data: > Fixed effects: > Arrivals: # of individuals arriving at the colony site in one-hour long intervals > TAS: Time After Sunset (factor with four categories: 3,4,5, &6) > MA: Moon Absence (ratio variable of the proportion of moon absent during the night, ranging from 0 (full > moon present) to 1 (no moon present)). > CC: Cloud Cover (ratio variable of proportion of sky covered by clouds, 0 = no clouds 1 = complete overcast sky. > WS: wind speed (ratio variable of wind speed in meters per second) > WH: wave height (ratio variable of wave height in meters) > Random effects: > JDOY: Julian Day of Year (factor: includes 50 days) > > Model: > glmm2<-glmmadmb(Arrivals~ (1|JDOY)+TAS+MA+CC+CWS+CWH+TAS*MA+TAS*CC+TAS*CWS+TAS*CWH+TAS*MA*CC+MA*CC, data = murrelet, family="nbinom") You don't need all those *: A*B is equivalent to A+B+A:B (in R : means 'interaction' (* in SAS), * means 'main effects plus all interactions'; I _think_ (1|JDOY)+TAS*MA*CC+TAS*(CWS+CWH) is equivalent. > > n=188 > You might be pushing these data too hard; what is the number of parameters (length(fixef(fitted_model)) or ncol(model.matrix(~TAS*MA*CC+TAS*(CWS+CWH),data=your_data) ...) You need 10-20 data points per parameter ... > This model runs fine (i.e., no errors). I have also run the same > model as a poisson, it also runs well, but the mean and variance are > not equal (hence the negative binomial distribution). I would like > to use AIC to draw inference from my data and have seven other > candidate models (the one shown above is the global model). To do > this, I need to extract an estimate of c-hat for the global model to > include in my calculation of QAICc for model selection. This is > where I get stuck. You don't need the Q part of QAICc; quasi-AIC(c)s are only needed to correct for overdispersion when you're using a response distribution (e.g. Poisson) that fixes the dispersion. For future reference, I think that in general *something* like sum(residuals(model)^2))/(nrow(data)-length(fixef(model))- (number of variance parameters) should give you c-hat ... _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology