Family mixtures, brms

I try to fit a model to predict log-transformed, neurophysiological data by age (values [0 1]). All variables are centered, the prior is a Cauchy distribution (0,2.5).

fit<-brm(la ~ age, data = d,control = list(adapt_delta = 0.95, max_treedepth=15),cores=4,warmup=1500, iter=3000, sample_prior=T, prior=prior1)

pp_check gives the following output:

Do you have any idea which family could be used to better describe this distribution? I tried mixture(gaussian,gaussian), which gave very bad results.


Hey @MartynaPlomecka,
I think instead of looking at your data and finding a distribution that matches it you should think about your data generating process and use the distribution that best matches that.
You could use the brms families list as a starting point.
There is also the Parameterization of Response Distributions vignette.

Eg. If you have discrete outcomes, don’t use a continuous likelihood, if you have negative values, don’t use a distribution that’s only positive and so on.

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