The p value for the random forest regression model So I was asked by a reviewer to provide the "P-value" for my random forest regression model. 
I tried to do some research on this, and only found methods to produce p values for each split condition (like in 'party' package), and p-values for variable importance (like in 'rfPermute' package). I find it hard to trust the p-values for variable importance since some very important variables have p-values of >0.9.
Any input on how to generate the general "p-value" for random forest (if there is one) would be appreciated. 
 A: When in doubt, simulate or permute.
In this specific case:


*

*Randomly permute your dependent variable.

*Fit a random forest.

*Note the % variance explained.


Do steps 1-3 multiple times, say 1,000-10,000 times. You now have an empirical distribution of % variance explained through a random forest, under the null hypothesis of no relationship between your independent and dependent variable.
Insert the actual % variance explained in your original model into this distribution, and note which proportion of permutation-based "null" % variance explained values exceeds this true value. This proportion is your p value.
If you did the same thing in a standard linear regression model, you would (asymptotically) get the p value for the classical F test for variance explained.
As others write, your reviewer does not sound overly statistically savvy, but the approach I'm outlining above makes sense and should satisfy him. It's better than getting into an anonymous argument over the statistical competence of a reviewer, anyway.
