I have a response variable ranging from 0 to 1, so I tried beta regression (stan_reg in rstanarm and used the beta family in brms). I chose beta because my variable represents a continuum from bad to good (more like a ratio). However, I have some observations with 0s and 1s, so when I fitted with the beta regression, it failed to fit since I should not have values either in 0s or 1s. So, which regression will better fit my analysis? I tried with normal regression, but there are just few values from 0 to 1. All the values are in 0.25 increments: 0, 0.25, 0.5, 0.75, and 1. I don't want to recode the variable since each value represent some condition which I don't want to miss. Is there a distribution, other than the ordinal distribution (which of course requires recoding), that will fit better represent this distribution?
This may be of interest to you.
Thank you so much professor. It is exactly the kind of regression analysis I was looking for. Much appreciated. For a week, I tried everything and I was unconvinced about my functional specification of the distribution. I think this regression analysis will fit my response distribution much better.
I should be clear, this is not my method and I am not a professor. Glad to hear it answers your question
With discrete values, you can use ordinal regression. Here’s a discussion in the User’s Guide:
The function itself is described here:
and also fully encapsulated in a higher-level function for use in a GLM setting: