Bookmark and Share

Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: st: importance of independent variables

From   Jeph Herrin <>
Subject   Re: st: importance of independent variables
Date   Thu, 06 Dec 2012 10:36:20 -0500

This doesn't exactly answer your specific questions, but one useful approach for assigning "importance" to independent variables is that of "random forests" (RF).

This is an algorithm which over many iterations selects a random subset of Xs and does a random split sample validated regression of the Ys against the Xs to produce a score. This generates a great big round robin tournament of Xs against each other, and results in a final "importance score" for each X, independent of the other Xs.

Unfortunately, I don't think anyone has implemented any version of this in Stata. Though I have considered doing so when I have a few spare days, so far I have only used it in R, which has RF algorithms available for linear, logit, and survival models (with multiple imputation for missing values of covariates that are not Xs).

hope this helps.


On 12/6/2012 4:17 AM, A. Berâ wrote:
Dear Stata Users,

I would like to compare the importance of independent variables (Xs)
in explaining the dependent variable (y).

What is the best way of doing this and how can this be done in Stata?

For example, how can I partition the variance of y into components
explained by individual Xs? Is there any user-written program that you
know of for this purpose?

Any help is appreciated. Thanks.

abdullah bera
*   For searches and help try:

*   For searches and help try:

© Copyright 1996–2017 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   Site index