fwdlm {forward} | 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 lmsreg (in package `lqs '. 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. |
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. |
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
.
data(forbes) plot(forbes, xlab="Boiling point", ylab="100 x log(pressure)") mod <- fwdlm(y ~ x, data=forbes) summary(mod) ## Not run: plot(mod) plot(mod, 1) plot(mod, 6, ylim=c(-3, 1000))