\HeaderA{summary.glm}{Summarizing Generalized Linear Model Fits}{summary.glm}
\aliasA{print.summary.glm}{summary.glm}{print.summary.glm}
\keyword{models}{summary.glm}
\keyword{regression}{summary.glm}
\begin{Description}\relax
These functions are all \code{\LinkA{methods}{methods}} for class \code{glm} or
\code{summary.glm} objects.
\end{Description}
\begin{Usage}
\begin{verbatim}
## S3 method for class 'glm':
summary(object, dispersion = NULL, correlation = FALSE,
        symbolic.cor = FALSE, ...)

## S3 method for class 'summary.glm':
print(x, digits = max(3, getOption("digits") - 3),
      symbolic.cor = x$symbolic.cor,
      signif.stars = getOption("show.signif.stars"), ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] an object of class \code{"glm"}, usually, a result of a
call to \code{\LinkA{glm}{glm}}.
\item[\code{x}] an object of class \code{"summary.glm"}, usually, a result of a
call to \code{summary.glm}.
\item[\code{dispersion}] the dispersion parameter for the fitting family.
By default it is obtained from \code{object}.
\item[\code{correlation}] logical; if \code{TRUE}, the correlation matrix of
the estimated parameters is returned and printed.
\item[\code{digits}] the number of significant digits to use when printing.
\item[\code{symbolic.cor}] logical. If \code{TRUE}, print the correlations in
a symbolic form (see \code{\LinkA{symnum}{symnum}}) rather than as numbers.
\item[\code{signif.stars}] logical. If \code{TRUE}, \dQuote{significance stars}
are printed for each coefficient.
\item[\code{...}] further arguments passed to or from other methods.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
\code{print.summary.glm} tries to be smart about formatting the
coefficients, standard errors, etc. and additionally gives
\dQuote{significance stars} if \code{signif.stars} is \code{TRUE}.

Aliased coefficients are omitted in the returned object but (as from \R{}
1.8.0) restored by the \code{print} method.

Correlations are printed to two decimal places (or symbolically): to
see the actual correlations print \code{summary(object)\$correlation}
directly.
\end{Details}
\begin{Value}
\code{summary.glm} returns an object of class \code{"summary.glm"}, a
list with components

\begin{ldescription}
\item[\code{call}] the component from \code{object}.
\item[\code{family}] the component from \code{object}.
\item[\code{deviance}] the component from \code{object}.
\item[\code{contrasts}] the component from \code{object}.
\item[\code{df.residual}] the component from \code{object}.
\item[\code{null.deviance}] the component from \code{object}.
\item[\code{df.null}] the component from \code{object}.
\item[\code{deviance.resid}] the deviance residuals:
see \code{\LinkA{residuals.glm}{residuals.glm}}.
\item[\code{coefficients}] the matrix of coefficients, standard errors,
z-values and p-values.  Aliased coefficients are omitted.
\item[\code{aliased}] named logical vector showing if the original
coefficients are aliased.
\item[\code{dispersion}] either the supplied argument or the estimated
dispersion if the latter in \code{NULL}
\item[\code{df}] a 3-vector of the rank of the model and the number of
residual degrees of freedom, plus number of non-aliased coefficients.
\item[\code{cov.unscaled}] the unscaled (\code{dispersion = 1}) estimated covariance
matrix of the estimated coefficients.
\item[\code{cov.scaled}] ditto, scaled by \code{dispersion}.
\item[\code{correlation}] (only if \code{correlation} is true.)  The estimated
correlations of the estimated coefficients.
\item[\code{symbolic.cor}] (only if \code{correlation} is true.)  The value
of the argument \code{symbolic.cor}.
\end{ldescription}
\end{Value}
\begin{SeeAlso}\relax
\code{\LinkA{glm}{glm}}, \code{\LinkA{summary}{summary}}.
\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
## --- Continuing the Example from  '?glm':

summary(glm.D93)
\end{ExampleCode}
\end{Examples}

