10.6084/m9.figshare.3530546.v1
Kim Murray
Kim
Murray
Mary M. Conner
Mary M.
Conner
Supplement 1. Source code for variable importance situations.
Wiley
2016
dominance analysis
beta coefficients
Akaike weights
hierarchical partitioning
partial correlation coefficients
beta weights
independent effects
standardized regression coefficients
relative weights
Environmental Science
Ecology
2016-08-05 09:15:30
Dataset
https://wiley.figshare.com/articles/dataset/Supplement_1_Source_code_for_variable_importance_situations_/3530546
<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>