Penrose Linear | R Documentation |

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.

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 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 |

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.

Norm Matloff

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