Supplement 1. Source code for variable importance situations.

2016-08-05T09:15:30Z (GMT) by Kim Murray Mary M. Conner
<h2>File List</h2><blockquote> <p><a href="Variable Importance Simulation.txt">Variable Importance Simulation.txt</a></p> <p><a href="Variable Importance Simulation.sas">Variable Importance Simulation.sas</a></p> <p><a href="hierpart.txt">hierpart.txt</a></p> <p><a href="hierpart.sas">hierpart.sas</a></p> </blockquote><h2>Description</h2><blockquote> <p>"Variable Importance Simulation.sas" is a simulation to evaluate the relative importance of random variables using Akaike weights, standardized regression coefficients, partial and semi-partial correlation coefficients, and hierarchical partitioning. <em>Remember</em> to change the subdirectory where hierpart.sas is called from the include statement.</p> <p>The file "hierpart.sas" is a macro that executes hierarchical partitioning analysis as described by Chevan and Sutherland in American Statistician, 1991, Vol. 45, no. 2, pp. 90–96. This macro was written by Kim Murray Berger and Mary M. Conner based on a Dominance Analysis macro written by Razia Azen and Robert Ceurvorst (<a href="http://www.uwm.edu/~azen/damacro.html">http://www.uwm.edu/~azen/damacro.html</a>). Hierarchical Partitioning analysis quantifies the importance of each predictor as its average contribution to the model r-square, across all possible models. <em>Note</em>: This program is limited to at most 10 predictors! </p> </blockquote>