# How to choose the appropiate beta regression model type and variables?

Recently, I got my hands on modelling proportions [0,1]. Due to data type many of my variables are 0 and 1 inflated. Some of them are delicately affected by the bound values and some are heavily. I performed three types of simple beta regression:

1. using betareg, fixed dispersion using y ~ x, logit link
2. using betareg, variable dispersion using y ~ x | x, logit link
3. using gamlss, BEINF family, logit link

Nevertheless, the results are substantially different between these three methods (i.e. they switch signs or shape). How we know which model for each variable in this case? The second quesiton is how we can compare the magnitude of several variables modeled by simple beta regressions if scalling is not applicable in this case?

@EDIT Example of predictions:

• As yet another model you could also use a heteroscedastic censored Gaussian model. This is available in the crch package via crch(y ~ x, left = 0, right = 1, ...). – Achim Zeileis May 14 '20 at 9:39