Hannes Brunner

I have a data set that is structured like this: A number of sites N_sites with varying number of censuses N_census. Within each census we have several individual observations obs(between 100 and 200). I am interested on the overall effect of b and the site and census specific effects of b1. My main question is did I implement this model correctly? The reason why am unsure about the way how I implemented the model is that the output contains a list named sims.list that I do not fully understand (see...

Hello everyone, I want to translate a nbinom1 model from glmmTMB to JAGS. But I don't know how to translate the dispersion formula to JAGS. For this example I use the nbinom2 because it is simpler but actually I would prefer to implement the dispersion model from the nbinom1 family from glmmTMB to JAGS. Note that the regression example here is completely out of context and does not make sense. This is only to provide code as the actual model I am working with is much more complex. We have a negative...

Hello everyone, I want to translate a nbinom1 model from glmmTMB to JAGS. But I don't know how to translate the dispersion formula to JAGS. For this example I use the nbinom2 because it is simpler but actually I would prefer to implement the dispersion model from the nbinom1 family from glmmTMB to JAGS. Note that the regression example here is completely out of context and does not make sense. This is only to provide code as the actual model I am working with is much more complex. We have a negative...

I want to use the same model for count data with varying lower measuring thresholds. A truncated poisson model allows me to do this. The model can successfully be defined in R. But I am not able to translate the same model to JAGS. The problem seems to be that it is not possible to divide a PDF by a CDF while defining the likelihood. This is what I tried: The data set.seed(42) N <- 10000 x1 <- runif(N, -1, 1) lower_limit = 7 upper_limit = Inf b0 <- 0.5 b1 <- 1.3 b2 <- 0.1 true_lambda <- exp(b0 +...