If frac_m is numeric (rather than a factor) then fitting frac_m as a predictor requires one extra beta coefficient, just as a linear regression has one coefficient for slope. The fitted relationship is linear on the link scale (so g0~frac_m fits a linear relationship between g0 and frac_m on the logit scale, unless you have perversely changed the link function; the default link for sigma is 'log').
To evaluate the predicted g0 for each value of sigma you need to provide the values of frac_m in a call to predict. In your case, maybe
- Code: Select all
[predict(secrDeerg0sigfm, newdata=data.frame(frac_m = covariates(traps(DeerCH))$frac_m))
However, (i) I haven't of course checked that, (ii) that generates a lot of output you probably don't want, and some further data extraction may be needed with e.g. sapply, and (iii) maybe you just want to plot a curve through a small number of well-spaced points.
As an aside: the code is far from optimal for trap covariates and I'm pleasantly surprised you got this to fit with lots of different values for frac_m: often it seems necessary to discretize a covariate like that so that it doesn't demand so much memory.