# Deal with NA's in power transformed data

I'm running a LASSO regression following this guide. I pre - processed my dependent variable using a simple power transformation to obtain a standard normal distribution. Unfortunately, this means I have NA's in my dependent variable, so I can't run LASSO using glmnet (returns: Error in elnet(x, is.sparse, ix, jx, y, weights, offset, type.gaussian, : NA/NaN/Inf in foreign function call (arg 6).

Is there anyway to overcome this?

• Your problem does not lie in the Lasso--it is due to choosing an inappropriate transformation of the dependent variable. – whuber Oct 20 '15 at 17:48

First, presumably you're getting NAs because the function you're using for the power transformation isn't returning real roots of negative real numbers, so either you've made a programming error—for example, you'd need to write your own function to return the real cube root of a negative real number rather than rely on ^(1/3)—or no real roots exist. If the latter, note that to avoid making assumptions about the data-generating process under which your observations are impossible is a sound principle. In any case you can hardly hope to proceed with analysis having mangled the data like this.