I have estimated the a zero-one-inflated beta regression model because my outcome variable has a lot of zeros and 1’s as well as some values between 0 and 1. The outcome variable is percent of milk sold to the market out of total amount produced. I would like to find out how I should interpret the output. I have regressors for the outcome variable (percentmilk), phi, zoi, and coi, terms. Any help will be appreciated. And thanks to @paul.buerkner for incorporating a zero-one-inflated beta regression into brms. Here is my simple code:
f1<-bf( percentmilk~age_hhh+age_sq+gender_hhh+single+household_size+educyears_hhh+dairyskills_hhh, phi~age_hhh+age_sq+gender_hhh+single+household_size+educyears_hhh+dairyskills_hhh, zoi~age_hhh+age_sq+gender_hhh+single+household_size+educyears_hhh+dairyskills_hhh, coi~age_hhh+age_sq+gender_hhh+single+household_size+educyears_hhh+dairyskills_hhh, family=zero_one_inflated_beta() ) model1<-brm(f1, data=finale, chains = 2, cores=2, warmup = 2000, iter = 5000, thin = 5, control = list(adapt_delta = 0.99)) My issue is how to interpret the posterior means for independent variables under percentmilk, phi, zoi, and coi. Any help is welcome. Thanks.