Correction: negative binomial mixed model using glmmadmb
Heather Major <heather.major at ...> 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 ...