mle.stepwise: Stepwise, Backward and Forward selection methods

In wle: Weighted Likelihood Estimation

Description

This function performs Stepwise, Forward and Backward model selection.

Usage

mle.stepwise(formula, data=list(), model=TRUE, x=FALSE, 
             y=FALSE, type="Forward", f.in=4.0, f.out=4.0, 
             contransts=NULL, verbose=FALSE)

Arguments

formula	a symbolic description of the model to be fit. The details of model specification are given below.
data	an optional data frame containing the variables in the model. By default the variables are taken from the environment which mle.stepwise is called from.
model, x, y	logicals. If TRUE the corresponding components of the fit (the model frame, the model matrix, the response.)
type	type="Stepwise": the stepwise methods is used, type="Forward": the forward methods is used, type="Backward": the backward method is used.
f.in	the in value
f.out	the out value
contransts	an optional list. See the contrasts.arg of model.matrix.default.
verbose	if TRUE warnings are printed.

Details

Models for mle.stepwise are specified symbolically. A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first+second indicates all the terms in first together with all the terms in second with duplicates removed. A specification of the form first:second indicates the the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first+second+first:second.

Value

mle.stepwise returns an object of class "mle.stepwise".

The function summary is used to obtain and print a summary of the results.

The object returned by mle.stepwise are:

step	the selected models
type	the type o model selection procedure was used.
f.in	the value of f.in used.
f.out	the value of f.out used.
call	the match.call().
contrasts
xlevels
terms	the model frame.
model	if model=TRUE a matrix with first column the dependent variable and the remain column the explanatory variables for the full model.
x	if x=TRUE a matrix with the explanatory variables for the full model.
y	if y=TRUE a vector with the dependent variable.
info	not well working yet, if 0 no error occurred.

Author(s)

Claudio Agostinelli

References

Beale, E.M.L., Kendall, M.G., Mann, D.W., (1967) The discarding of variables in multivariate analysis, Biometrika, 54, 357-366.

Efroymson, (1960) Multiple regression analysis, in Mathematical Methods for Digital Computers, eds. A. Ralston and H.S. Wilf, 191-203, Wiley, New York.

Garside, M.J., (1965) The best sub-set in multiple regression analysis, Applied Statistics, 14, 196-200.

Goldberger, A.S, and Jochems, D.B., (1961) Note on stepwise least squares, Journal of the American Statistical Association, 56, 105-110.

Goldberger, A.S., (1961) Stepwise least squares: Residual analysis and specification error, Journal of the American Statistical Association, 56, 998-1000.

Examples

library(wle)

data(hald)

cor(hald)

result <- mle.stepwise(y.hald~x.hald)

summary(result)

wle documentation built on May 29, 2017, 11:48 a.m.