I know that BRMS can do two interesting things, but I have thus far not been able to do both at once.
I have a bunch of images rated 1-5. I tried to create a model that includes sigma like so:
bform <- bf(rate ~ s(b,s),
sigma~ s(b,s))
brm(bform, data = outsub, chains = 2, cores = 2, warmup = 2000, iter=4000)
This model assumes that the data is gaussian distributed, which is not true, but the model that also predicts sigma is about 20SEs better when I compare it to one that is simply specified:
bform <- bf(rate ~ s(b,s)
It thus seems like it could be valuable to include this in an ordinal model specified as the following:
brm(rating ~ category+distortion + (1|id),
family = cumulative(threshold = "flexible")
I can specify this model too without issues, but when I try to combine them in
brm(bform,family = cumulative(threshold = "flexible")
… I get the error:
Error: The parameter 'sigma' is not a valid distributional or non-linear parameter.
Did you forget to set 'nl = TRUE'?
I tried to read through some documentation such as Estimating Distributional Models with brms and it seems like this may only be a feature that works for gaussian and a few other distributions. Is this the case and if so should I simply give up on my attempt to model this as ordinal data for the time being? I am happy to provide data if needed, but given the question’s conceptual nature I chose not to do so for the time being.
Thanks for reading though my question