I am doing a Multiple Linear Regression on a data set where: The response variable is continuous One of the explanatory variables is continuous and the rest are binary(categorical) 1 if it is there 0 if it is not.
I did the Multiple linear regression on my data and found that it had non constant variance so I used Box Cox transformation.
The Box Cox transformation seemed to have worked very well. It had good residual vs. fitted values plots, residuals with a normal distibution and good r-squared and adjusted r-squared values.
The data I did the Box Cox transformation on was a training set. I now need to perform a model validation on the test set. I am using R to do my calculations. When I use the
predict function in R the predicted values will be in the transformed state.
I would also like to use the
cv.lm function in R which performs a cross validation using a given model and a data set. When I used this I am not quite sure which data set to use. The original or the transformed. Information on
cv.lm can be found here http://www.statmethods.net/stats/regression.html and http://www.inside-r.org/packages/cran/DAAG/docs/CVlm
My questions are:
Once I have the predicted values can I just use the inverse of the Box Cox to get my values back to original?
If not how do I proceed from here to make sense of my model? I have looked a lot of places online and would really like some insight or expertise in this.
Thanks in advance.