I'm working with a wild chimpanzee behavioural dataset (one individual) that has data points collected closely following each other in time and space.
My model now looks as follows:
onHR ~ z.no_indep_log+z.MRatio + sin(date.rad)+ cos(date.rad)+rain*I(z.time^2)+on_off_trail, data=xdata, family=binomial("logit")
I'm attempting to find out the influence of group size and formation on the probability of using a habitual route (on/off HR). Other predictors in my model are seasonal variation, weather conditions, and whether or not the route is on a human-made research trail.
Acf lag 1 is 0.865
How can I, in R, control for the autocorrelation caused by the fact that this data is from a time-series, without having to include a random effect?