Alternative for transformation of predictors I am working with multiple linear regression models and am attempting to transform the predictors of the model. I am attempting to use the powertransform() function however since my data contains zeros, doing so returns an error stating that the first value must be strictly postive. Is there an alternative to the powertransform function when data contains zeros?  The data looks as below. I would like to transform it in order to get a better linear regression model
 A: Well one way is to just add one to every value and then transform, Powertransform$(x+1)$. I assume this is some type of Box-Cox transform. 
But, looking at your picture it looks like a very large number of the entries are zero. If virtually every value is zero, there is not much information that the column can provide, so it might be better to drop the column and create a model using the other columns.
If there are a few but a nontrial number of non-zero values, it might also be good to make all the non-zero values one and make the column a categorical variable with two levels, zero and one. Here you are just trying to detect the situation that would make it non-zero.
If there are a reasonable number of non-zero values, but the distribution of the column is non-normal, you can make it zero-one as above, or bin the predictor into some reasonable number of levels, or standardize the column, ie subtract the mean and divide by the sd. 
It should be noted that the requirement of linear regression is not normal predictors OR normal response, rather, normal residuals after the regression model has been fit.
There are possibly other things I can't think of off the top of my head right now.
