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Has anyone tried implementing beta transformed regression python? It is used to model values that lie between 0 and 1 and has a distribution with heteroskedasticity ineherently present. Essentially you first transform the dependent variable to a beta distribution and apply a second transformation or a link function such as logit or probit to restrict its values bw 0 and 1. The link below explains in detail the code for R: https://cran.r-project.org/web/packages/betareg/vignettes/betareg.pdf

However, I'm having a hard time finding functions/libraries to do this in python. Even if i try using brute force method, once we have the predicted transformed Ys, transforming it back to original Ys is challenging. Moreover I don't want to go the brute force route and was wondering if there is a sophisticated way to implement this in python. Thanks a lot!

user8947896
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Apologies for the link-only answer (with which I am not associated) - but look at the following for this:

https://towardsdatascience.com/a-guide-to-the-regression-of-rates-and-proportions-bcfe1c35344f

jtlz2
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