fwdlm: Forward Search in Linear Regression
In forward: Robust Analysis using Forward Search

fwdlm

R Documentation

Forward Search in Linear Regression

Description

This function applies the forward search approach to robust analysis in linear regression models.

Usage

fwdlm(formula, data, nsamp = "best", 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 are used 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`.
`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 "fwdlm" with the following components:

`call`	the matched call.
`Residuals`	a `(n \times (n-p+1))` matrix of residuals.
`Unit`	a matrix of units added (to a maximum of 5 units) at each step.
`included`	a list with each element containing a vector of units included at each step of the forward search.
`Coefficients`	a `((n-p+1) \times p)` matrix of coefficients.
`tStatistics`	a `((n-p+1) \times p)` matrix of t statistics for the coefficients.
`CookDist`	a `((n-p) \times 1)` matrix of forward Cook's distances.
`ModCookDist`	a `((n-p) \times 5)` matrix of forward modified Cook's distances for the units (to a maximum of 5 units) included at each step.
`Leverage`	a `(n \times (n-p+1))` matrix of leverage values.
`S2`	a `((n-p+1) \times 2)` matrix with 1st column containing `S^2` and the 2nd column `R^2`.
`MaxRes`	a `((n-p) \times 1)` matrix of max studentized residuals.
`MinDelRes`	a `((n-p-1) \times 1)` matrix of minimum deletion residuals.
`StartingModel`	a `"lqs"` object providing the the Least Median of Squares regression fit used to select the starting subset.

Author(s)

Originally written for S-Plus by: Kjell Konis kkonis@insightful.com and Marco Riani mriani@unipr.it
Ported to R by Luca Scrucca luca@stat.unipg.it

References

Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New York: Springer, Chapters 2-3.

Examples

library(MASS)
data(forbes)
plot(forbes, xlab="Boiling point", ylab="Pressure)")
mod <- fwdlm(100*log10(pres) ~ bp, data=forbes)
summary(mod)
## Not run: plot(mod)
plot(mod, 1)
plot(mod, 6, ylim=c(-3, 1000))

forward documentation built on Sept. 27, 2024, 5:09 p.m.