FWDselect (version 1.1)

selection: Selecting a subset of q variables

Description

Main function for selecting the best subset of $q$ variables. Note that the selection procedure can be used with lm, glm or gam functions.

Usage

selection(x, y, q, criterion = "deviance", method = "lm", family = "gaussian", 
seconds = FALSE, nmodels = 1)

Arguments

x

A data frame containing all the covariates.

y

A vector with the response values.

q

An integer specifying the size of the subset of variables to be selected.

criterion

The cross-validation-based information criterion to be used. Default is the deviance. Other functions provided are the coefficient of determination ("R2") and residual variance ("variance").

method

A character string specifying which regression method is used, i.e., linear models ("lm"), generalized additive models ("glm") or generalized additive models ("gam").

family

This is a family object specifying the distribution and link to use in fitting: "gaussian", "binomial" or "poisson".

seconds

A logical value. By default, FALSE. If TRUE then, rather than returning the single best model only, the function returns a few of the best models (equivalent).

nmodels

Number of secondary models to be returned.

Value

• Best model
  The best model. If seconds=TRUE, it returns also the best alternative models.
• Variable name
  Names of the variable.
• Variable number
  Number of the variables.
• Information criterion
  Information criterion used and its value.
• Prediction
  The prediction of the best model.

Examples

library(FWDselect)
data(pollution)
x=pollution[,-19]
y=pollution[,19]
obj1=selection(x,y,q=1,method="lm",criterion="deviance")
obj1

obj11=selection(x,y,q=1,method="lm",criterion="deviance",seconds=TRUE,nmodels=2)
obj11