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\name{slm.methods}
\alias{slm.methods}
\alias{summary.slm}
\alias{summary.mslm}
\alias{print.summary.slm}
\alias{print.slm}
\alias{fitted.slm}
\alias{residuals.slm}
\alias{coef.slm}
\alias{extractAIC.slm}
\alias{deviance.slm}
\title{Methods for slm objects}
\description{
Summarize, print, and extract objects from \code{\link{slm}} objects.
}
\usage{
\method{summary}{slm}(object, correlation, \dots)
\method{summary}{mslm}(object, \dots)
\method{print}{slm}(x, digits, \dots)
\method{print}{summary.slm}(x, digits, symbolic.cor, signif.stars, \dots)
\method{fitted}{slm}(object, \dots)
\method{residuals}{slm}(object, \dots)
\method{coef}{slm}(object, \dots)
\method{extractAIC}{slm}(fit, scale = 0, k = 2, \dots)
\method{deviance}{slm}(object, \dots)
}
\arguments{
\item{object,x,fit}{ object of class \code{slm}. }
\item{digits}{minimum number of significant digits to be used for most numbers.}
\item{scale}{optional numeric specifying the scale parameter of the model,
see 'scale' in 'step'. Currently only used in the '"lm"'
method, where 'scale' specifies the estimate of the error
variance, and 'scale = 0' indicates that it is to be
estimated by maximum likelihood.}
\item{k}{ numeric specifying the "weight" of the equivalent degrees of
freedom ('edf') part in the AIC formula.}
\item{symbolic.cor}{logical; if \code{TRUE}, the correlation of coefficients
will be printed. The default is \code{FALSE}}
\item{signif.stars}{logical; if \code{TRUE}, P-values are additionally encoded
visually as ``significance stars'' in order to help scanning
of long coefficient tables. It defaults to the
`show.signif.stars' slot of `options'. }
\item{correlation}{logical; if \code{TRUE}, the correlation matrix of the
estimated parameters is returned and printed.}
\item{\dots}{additional arguments passed to methods.}
}
\value{
\code{print.slm} and \code{print.summary.slm} return invisibly.
\code{fitted.slm}, \code{residuals.slm}, and \code{coef.slm}
return the corresponding components of the \code{slm} object.
\code{extractAIC.slm} and \code{deviance.slm} return the AIC
and deviance values of the fitted object.
}
\references{
Koenker, R and Ng, P. (2002). SparseM: A Sparse Matrix Package for \R,\cr
\url{http://www.econ.uiuc.edu/~roger/research/home.html}
}
\author{ Roger Koenker }
\seealso{
\code{slm}
}
\examples{
data(lsq)
X <- model.matrix(lsq) #extract the design matrix
y <- model.response(lsq) # extract the rhs
X1 <- as.matrix(X)
slm.time <- system.time(slm(y~X1-1) -> slm.o) # pretty fast
cat("slm time =",slm.time,"\n")
cat("slm Results: Reported Coefficients Truncated to 5 ","\n")
sum.slm <- summary(slm.o)
sum.slm$coef <- sum.slm$coef[1:5,]
sum.slm
fitted(slm.o)[1:10]
residuals(slm.o)[1:10]
coef(slm.o)[1:10]
}
\keyword{ regression }
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