forward (version 1.0.3)

fwdsco: Forward Search Transformation in Linear Regression

Description

This function applies the forward search approach to the Box-Cox transformation of response in linear regression models.

Usage

fwdsco(formula, data, nsamp = "best", lambda = c(-1, -0.5, 0, 0.5, 1),  x = NULL, y = NULL, intercept = TRUE, na.action, trace = TRUE)

Arguments

formula

a symbolic description of the model to be fit. The details of the model are the same as for lm.

data

an optional data frame containing the variables in the model. By default the variables are taken from the environment from which the function is called.

nsamp

the initial subset for the forward search in linear regression is found by fitting the regression model with the R function lmsreg. This argument allows to control how many subsets areused in the Least Median of Squares regression. The choices are: the number of samples or "best" (the default) or "exact" or "sample". For details see lmsreg.

lambda

a vector (or a single numerical value) of lambda values for the response transformation.

x

A matrix of predictors values (if no formula is provided).

y

A vector of response values (if no formula is provided).

intercept

Logical for the inclusion of the intercept (if no formula is provided).

na.action

a function which indicates what should happen when the data contain NA's. The default is set by the na.action setting of options, and is na.fail if that is unset. The default is na.omit.

trace

logical, if TRUE a message is printed for every ten iterations completed during the forward search.

Value

The function returns an object of class"fwdsco" with the following components:

call

the matched call.

Likelihood

a $((n-p+1) x n.lambda)$ matrix of likelihood values.

ScoreTest

a $((n-p+1) x n.lambda)$ matrix of score test statistic values.

Unit

a list with an element for each lambda values. Each element provides a matrix of units added (to a maximum of 5 units) at each step of the forward search.

Input

a list with $n$, $p$ and the vector of lambda values used.

x

The design matrix.

y

The vector for the response.

References

Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New York: Springer, Chapter 4.

See Also

summary.fwdsco, plot.fwdsco, fwdlm, fwdglm.

Examples

data(wool)
mod <- fwdsco(y ~ x1 + x2 + x3, data = wool)
summary(mod)
plot(mod, plot.mle=FALSE)
plot(mod, plot.Sco=FALSE, plot.Lik=TRUE)