Description Details Author(s) References See Also Examples
The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods (Friendly, 2012). These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors.
Package: | genridge |
Type: | Package |
Version: | 0.6-5 |
Date: | 2014-11-24 |
License: | GPL version 2 or newer |
LazyLoad: | yes |
This package provides computational support
for the graphical methods described in Friendly (2012).
Ridge regression models may be fit using the function ridge
,
which incorporates features of lm.ridge
and
simple.ridge
. In particular, the shrinkage
factors in ridge regression may be specified either in terms of
the constant added to the diagonal of X^T X matrix (lambda
),
or the equivalent number of degrees of freedom.
More importantly, the ridge
function also calculates and
returns the associated covariance matrices of each of the ridge estimates,
allowing precision to be studied and displayed graphically.
This provides the support for the main plotting functions in the package:
plot.ridge
: Bivariate ridge trace plots
pairs.ridge
: All pairwise bivariate ridge trace plots
plot3d.ridge
: 3D ridge trace plots
traceplot
: Traditional univariate ridge trace plots
In addition, the function pca.ridge
transforms the coefficients
and covariance matrices of a ridge
object from predictor space to the
equivalent, but more interesting space of the PCA of X^T X or the
SVD of X.
The main plotting functions also work for these objects,
of class c("ridge", "pcaridge")
.
Finally, the functions precision
and vif.ridge
provide other useful measures and plots.
Michael Friendly
Maintainer: Michael Friendly <friendly@yorku.ca>
Friendly, M. (2012). The Generalized Ridge Trace Plot: Visualizing Bias and Precision. In press, Journal of Computational and Graphical Statistics, 21.
Arthur E. Hoerl and Robert W. Kennard (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems, Technometrics, 12(1), pp. 55-67.
Arthur E. Hoerl and Robert W. Kennard (1970). Ridge Regression: Applications to Nonorthogonal Problems Technometrics, 12(1), pp. 69-82.
1 | # see examples for ridge, etc.
|
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