Penrose Linear {regtools} | R Documentation |
Penrose-Inverse Linear Models and Polynomial Regression
Description
Provides mininum-norm solutions to linear models, identical to OLS in standard situations, but allowing exploration of overfitting in the overparameterized case. Also provides a wrapper for the polynomial case.
Usage
penroseLM(d,yName)
penrosePoly(d,yName,deg,maxInteractDeg=deg)
ridgePoly(d,yName,deg,maxInteractDeg=deg)
## S3 method for class 'penroseLM'
predict(object,...)
## S3 method for class 'penrosePoly'
predict(object,...)
Arguments
... |
Arguments for the |
d |
Dataframe, training set. |
yName |
Name of the class labels column. |
deg |
Polynomial degree. |
maxInteractDeg |
Maximum degree of interaction terms. |
object |
A value returned by |
Details
First, provides a convenient wrapper to the polyreg package for
polynomial regression. (See qePoly
here for an even higher-level
wrapper.) Note that this computes true polynomials, with
cross-product/interaction terms rather than just powers, and that dummy
variables are handled properly (to NOT compute powers).
Second, provides a tool for exploring the "double descent" phenomenon, in which prediction error may improve upon fitting past the interpolation point.
Author(s)
Norm Matloff