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Why is it that Beta Regression that is bound between 0 and 1 is unable to handle lots of independent variables as Regressors? I have around 30 independent variables that I am trying to fit and it shows error like:

Error in optim(par = start, fn = loglikfun, gr = gradfun, method = method, : non-finite value supplied by optim

Only few variables it is accepting.Now If I combine all these independent variables in X <- (df$x1 + … + df$x30) and make dependent variable in Y <- df$y and then run Beta Regression then it works but I won’t be getting coefficients for individual independent variables which I want.

betareg(Y ~ X, data = df)

So, what’s the solution?

Basti
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sammy0722
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  • It's easier to help you if you include a simple [reproducible example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) with sample input that can be used to test and verify possible solutions. How many observations do you have compared to the number of variables. For more general model discussions, it may be better to ask at [stats.se]. Stack Overflow is for specific programming questions and if we can't run the code ourselves, it's hard to help. – MrFlick Jun 23 '21 at 06:22

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Probably, the model did not converge because of the multicollinearity problem. In most cases, regression models can not be estimated properly when lots of variables are considered. You can overcome this problem with an appropriate variable selection procedure using information criteria.

You can benefit gamlss package in R. Also, stepGAIC() function can help you when considering gamlss(...,family=BE) function during the modeling.

Emre DÜNDER
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