effect size
From help(relimp, package="relimp"):
Details:
If 'set1' and 'set2' both have length 1, relative importance is
measured by the ratio of the two standardized coefficients.
Equivalently this is the ratio of the standard deviations of the
two contributions to the linear predictor, and this provides the
generalization to comparing two sets rather than just a pair of
predictors.
Doesn't look like what David want (he's not comparing factor to factor).
Andy
From: Andrew Robinson [mailto:andrewr at uidaho.edu]
Thinking about effect sizes, a plausible alternative may be
found in the
relimp package.
From CRAN: relimp: Relative Contribution of Effects in a
Regression Model
Functions to facilitate inference on the relative importance
of predictors in
a linear or generalized linear model
Version: 0.8-2
Depends: R (>= 1.8.0), tcltk, MASS
Author: David Firth
Maintainer: David Firth <d.firth at warwick.ac.uk>
License: GPL (version 2 or later)
URL: http://www.warwick.ac.uk/go/relimp
http://www.warwick.ac.uk/go/dfirth
Andrew
--
Andrew Robinson Ph: 208 885 7115
Department of Forest Resources Fa: 208 885 6226
University of Idaho E : andrewr at uidaho.edu
PO Box 441133 W :
http://www.uidaho.edu/~andrewr
Moscow ID 83843
Or: http://www.biometrics.uidaho.edu
No statement above necessarily represents my employer's opinion.
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