I am fitting an ordinal regression that accounts for unequal variances and category specific effects of the variables. Before running the model with the whole dataset, I have fitted the model with only one variable with category specific effects. To my surprising the model is too slow and is taking days to complete.
Given that I only used one variable, the whole dataset consists of only 926 observations, and the model ran fast when I did not consider category specific effects, I am wondering (1) if there is something wrong in the model specification,(2) what to do to increase the speed. I have attached the code and the data. Thanks for your help in advance.
note: X1 is a categorical variable
fit_1<- brm(bf(Y~cs(X1))+lf(disc~0+cs(X1),cmc = FALSE),data =wwd, family = acat("probit"), chains=2,inits=0,warmup = 1000,iter = 2000,algorithm = "sampling", seed = 125, control = list(adapt_delta = 0.9,max_treedepth = 20))
fit_1_data.csv (7.6 KB)