The R Project SVN R

\name{lm.summary}
\alias{summary.lm}
\alias{summary.lm.null}
\alias{summary.mlm}% mlm is *not* mentioned at all
\alias{print.summary.lm}
\alias{print.summary.lm.null}
\title{Summarizing Linear Model Fits}
\usage{
\method{summary}{lm}(object, correlation = FALSE, \dots)

\method{print}{summary.lm}(x, digits = max(3, getOption("digits") - 3),
      symbolic.cor = p > 4,
      signif.stars = getOption("show.signif.stars"), \dots)
}
\arguments{
  \item{object}{an object of class \code{"lm"}, usually, a result of a
    call to \code{\link{lm}}.}
  \item{x}{an object of class \code{"summary.lm"}, usually, a result of a
    call to \code{summary.lm}.}
  \item{correlation}{logical; if \code{TRUE}, the correlation matrix of
    the estimated parameters is returned and printed.}
  \item{digits}{the number of significant digits to use when printing.}
  \item{symbolic.cor}{logical. If \code{TRUE}, print the correlations in
    a symbolic form (see \code{\link{symnum}} rather than as numbers.}
  \item{signif.stars}{logical. If \code{TRUE}, ``significance stars''
    are printed for each coefficient.}
  \item{\dots}{further arguments passed to or from other methods.}
}
\description{
\code{summary} method for class \code{"lm"}.
}
\details{
\code{print.summary.lm} tries to be smart about formatting the
coefficients, standard errors, etc. and additionally gives
``significance stars'' if \code{signif.stars} is \code{TRUE}.
}
\value{
  The function \code{summary.lm} computes and returns a list of summary
  statistics of the fitted linear model given in \code{object}, using
  the components (list elements) \code{"call"} and \code{"terms"}
  from its argument, plus
  \item{residuals}{the \emph{weighted} residuals, the usual residuals
    rescaled by the square root of the weights specified in the call to
    \code{lm}.}
  \item{coefficients}{a \eqn{p \times 4}{p x 4} matrix with columns for
    the estimated coefficient, its standard error, t-statistic and
    corresponding (two-sided) p-value.}
  \item{sigma}{the square root of the estimated variance of the random
    error
    \deqn{\hat\sigma^2 = \frac{1}{n-p}\sum_i{R_i^2},}{%
      sigma^2 = 1/(n-p) Sum(R[i]^2),}
    where \eqn{R_i}{R[i]} is the \eqn{i}-th residual, \code{residuals[i]}.}
  \item{df}{degrees of freedom, a 3-vector \eqn{(p, n-p, p*)}.}
  \item{fstatistic}{a 3-vector with the value of the F-statistic with
    its numerator and denominator degrees of freedom.}
  \item{r.squared}{\eqn{R^2}, the ``fraction of variance explained by
    the model'',
    \deqn{R^2 = 1 - \frac{\sum_i{R_i^2}}{\sum_i(y_i- y^*)^2},}{%
      R^2 = 1 - Sum(R[i]^2) / Sum((y[i]- y*)^2),}
    where \eqn{y^*}{y*} is the mean of \eqn{y_i}{y[i]} if there is an
    intercept and zero otherwise.}
  \item{adj.r.squared}{the above \eqn{R^2} statistic
    \emph{``adjusted''}, penalizing for higher \eqn{p}.}
  \item{cov.unscaled}{a \eqn{p \times p}{p x p} matrix of (unscaled)
    covariances of the \eqn{\hat\beta_j}{coef[j]}, \eqn{j=1, \dots, p}.}
  \item{correlation}{the correlation matrix corresponding to the above
    \code{cov.unscaled}, if \code{correlation = TRUE} is specified.}
}
\seealso{
  The model fitting function \code{\link{lm}}, \code{\link{summary}}.
}
\examples{
\testonly{example("lm", echo = FALSE)}
##-- Continuing the  lm(.) example:
coef(lm.D90)# the bare coefficients
sld90 <- summary(lm.D90 <- lm(weight ~ group -1))# omitting intercept
sld90
coef(sld90)# much more
}
\keyword{regression}
\keyword{models}