Hi List, I have a presence-absence fish telemetry dataset that has a very high number of zero's (12,732 absences and 1120 presences). So I want fit a cloglog link GLMM, which seems to work better than a logit link. However, I also have some quasi-complete seperation- that is I have all zeros in one of my factor level predictions. So ideally I would like to try and fit zero mean normal priors for my fixed effects levels using Ben Bolker's logit link example https://ms.mcmaster.ca/~bolker/R/misc/foxchapter/bolker_chap.html using bglmer. so the logit model looks like this: cmod_blme_L2 <- bglmer(predation~ttt+(1|block),data=newdat, family=binomial, fixef.prior = normal(cov = diag(9,4))) where ttt is a categorical variable four levels the priors provide 4 ? 4 diagonal matrix with diagonal elements equal to 9, for variances of 9 or standard deviations of 3. So with the logit link above -2sd will be -6 and +2SD would be +6 ...so pretty weak. inv.logit(-6)<- 0.002472623 inv.logit(6)<- 0.9975274 Can I use the same priors for cloglog link? invcloglog(-6) <- 0.002475683 invcloglog(6)<- 1 My gut says yes it should work they are both weak and the issues are with the lower end. The model runs nicely and gives sensible estimates. However I figured it would be more correct to have 2xsd be -6 and +2 but I dont know how to code such a prior. I would consider using MCMCglmm too if it allowed me to fit those priors. Any help would be much appreciated Philip Harrison PhD Post-Doc in Cooke and Power Labs Department of Biology University of Waterloo 200 University Avenue West Waterloo, Ontario, Canada N2L 3G1 Researchgate: http://tinyurl.com/RG-PMH Google Scholar: http://tinyurl.com/ScholarPMH Lab Website: http://www.fecpl.ca/people/philip-harrison/ Personal Website: https://pharriso4.wixsite.com/philipmharrison
zero mean fixed effects priors in a cloglog link model
1 message · Philip Harrison