Hi,
I get the following error message when I try to fit a multisession model with a temporal covariate (jc) affecting sigma:
> gDT.sjc=secr.fit(seeh69, model=list(g0~1, sigma~jc), mask=masklist,CL=T,detectfn=1,timecov=timecovs,sessioncov=sesscovs,details=list(distribution='binomial'), stepmax=50)
Checking data
Preparing detection design matrices
Finding initial parameter values...
Initial values D = 0.00088, g0 = 0.36223, sigma = 1275.15136
Maximizing likelihood...
Eval Loglik g0 sigma sigma.jc z
1 -5543.359 -0.5657 7.1508 0.0000 1.6094
2 -5543.359 -0.5657 7.1508 0.0000 1.6094
3 -5543.359 -0.5657 7.1508 0.0000 1.6094
4 -5543.349 -0.5657 7.1508 0.0000 1.6094
5 -5543.213 -0.5657 7.1508 0.0000 1.6094
6 -5543.361 -0.5657 7.1508 0.0000 1.6094
beta vector : -0.5113226 7.658609 49.99597 1.233034
Error in f(x, ...) :
extreme beta in secr.loglikfn (try smaller stepmax in nlm Newton-Raphson?)
I had already defined stepmax = 50, and tried bumping it down to 10 with no change.
My "timecovs" dataframe looks like this:
> timecovs
occasion jc
1 1 66
2 2 78
3 3 90
4 4 100
5 5 100
6 6 100
The dataset includes sessions with 4, 5, and 6 occasions, and secr documentation specifies that the lenght of timecov should equal the number of occasions, however, I didn't have this problem when fitting similar models to similar data (multiple sessions with different numbers of occasions) using secr v 1.3.0.
Any insights appreciated.
Eric