lmSubsets_fit: All-subsets regression

In lmSubsets: Exact Variable-Subset Selection in Linear Regression

Description

Low-level interface to all-variable-subsets selection in ordinary linear regression.

Usage

lmSubsets_fit(x, y, weights = NULL, offset = NULL, include = NULL,
              exclude = NULL, nmin = NULL, nmax = NULL,
              tolerance = 0, nbest = 1, ..., pradius = NULL)

Arguments

x	double[,]—the model matrix
y	double[]—the model response
weights	double[]—the model weights
offset	double[]—the model offset
include	logical[], integer[], character[]—the regressors to force in
exclude	logical[], integer[], character[]—the regressors to force out
nmin	integer—the minimum number of regressors
nmax	integer—the maximum number of regressors
tolerance	double[]—the approximation tolerances
nbest	integer—the number of best subsets
...	ignored
pradius	integer—the preordering radius

Details

The best variable-subset model for every subset size is determined, where the "best" model is the one with the lowest residual sum of squares (RSS).

The regression data is specified with the x, y, weights, and offset parameters. See lm.fit() for further details.

To force regressors into or out of the regression, a list of regressors can be passed as an argument to the include or exclude parameters, respectively.

The scope of the search can be limited to a range of subset sizes by setting nmin and nmax, the minimum and maximum number of regressors allowed in the regression, respectively.

A tolerance vector can be specified to speed up the search, where tolerance[j] is the approximation tolerance applied to subset models of size j.

The number of submodels returned for each subset size is determined by the nbest parameter.

The preordering radius is given with the pradius parameter.

Value

A list with the following components:

NOBS	integer—number of observations in model (before weights processing)
nobs	integer—number of observations in model (after weights processing)
nvar	integer—number of regressors in model
weights	double[]—model weights
intercept	logical—is TRUE if model contains an intercept term, FALSE otherwise
include	logical[]—regressors forced into the regression
exclude	logical[]—regressors forced out of the regression
size	integer[]—subset sizes
tolerance	double[]—approximation tolerances
nbest	integer—number of best subsets
submodel	"data.frame"—submodel information
subset	"data.frame"—variable subsets

References

Hofmann M, Gatu C, Kontoghiorghes EJ, Colubi A, Zeileis A (2020). lmSubsets: Exact variable-subset selection in linear regression for R. Journal of Statistical Software, 93, 1–21. doi: 10.18637/jss.v093.i03.

See Also

• lmSubsets() for the high-level interface

• lmSelect_fit() for best-subset regression

Examples

data("AirPollution", package = "lmSubsets")

x <- as.matrix(AirPollution[, names(AirPollution) != "mortality"])
y <-           AirPollution[, names(AirPollution) == "mortality"]

f <- lmSubsets_fit(x, y)
f

lmSubsets documentation built on Feb. 8, 2021, 1:06 a.m.