fwdlm | R Documentation |
This function applies the forward search approach to robust analysis in linear regression models.
fwdlm(formula, data, nsamp = "best", x = NULL, y = NULL, intercept = TRUE, na.action, trace = TRUE)
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 |
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 |
trace |
logical, if |
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 |
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
Atkinson, A.C. and Riani, M. (2000), Robust Diagnostic Regression Analysis, First Edition. New York: Springer, Chapters 2-3.
summary.fwdlm
, plot.fwdlm
, fwdsco
, fwdglm
, lmsreg
.
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))
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