| % File src/library/stats/man/summary.glm.Rd |
| % Part of the R package, https://www.R-project.org |
| % Copyright 1995-2013 R Core Team |
| % Distributed under GPL 2 or later |
| |
| \name{summary.glm} |
| \alias{summary.glm} |
| \alias{print.summary.glm} |
| \title{Summarizing Generalized Linear Model Fits} |
| \usage{ |
| \method{summary}{glm}(object, dispersion = NULL, correlation = FALSE, |
| symbolic.cor = FALSE, \dots) |
| |
| \method{print}{summary.glm}(x, digits = max(3, getOption("digits") - 3), |
| symbolic.cor = x$symbolic.cor, |
| signif.stars = getOption("show.signif.stars"), \dots) |
| } |
| \arguments{ |
| \item{object}{an object of class \code{"glm"}, usually, a result of a |
| call to \code{\link{glm}}.} |
| \item{x}{an object of class \code{"summary.glm"}, usually, a result of a |
| call to \code{summary.glm}.} |
| \item{dispersion}{the dispersion parameter for the family used. |
| Either a single numerical value or \code{NULL} (the default), when |
| it is inferred from \code{object} (see \sQuote{Details}).} |
| \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}, \sQuote{significance stars} |
| are printed for each coefficient.} |
| \item{\dots}{further arguments passed to or from other methods.} |
| } |
| \description{ |
| These functions are all \code{\link{methods}} for class \code{glm} or |
| \code{summary.glm} objects. |
| } |
| \details{ |
| \code{print.summary.glm} tries to be smart about formatting the |
| coefficients, standard errors, etc. and additionally gives |
| \sQuote{significance stars} if \code{signif.stars} is \code{TRUE}. |
| The \code{coefficients} component of the result gives the estimated |
| coefficients and their estimated standard errors, together with their |
| ratio. This third column is labelled \code{t ratio} if the |
| dispersion is estimated, and \code{z ratio} if the dispersion is known |
| (or fixed by the family). A fourth column gives the two-tailed |
| p-value corresponding to the t or z ratio based on a Student t or |
| Normal reference distribution. (It is possible that the dispersion is |
| not known and there are no residual degrees of freedom from which to |
| estimate it. In that case the estimate is \code{NaN}.) |
| |
| Aliased coefficients are omitted in the returned object but 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. |
| |
| The dispersion of a GLM is not used in the fitting process, but it is |
| needed to find standard errors. |
| If \code{dispersion} is not supplied or \code{NULL}, |
| the dispersion is taken as \code{1} for the \code{binomial} and |
| \code{Poisson} families, and otherwise estimated by the residual |
| Chisquared statistic (calculated from cases with non-zero weights) |
| divided by the residual degrees of freedom. |
| |
| \code{summary} can be used with Gaussian \code{glm} fits to handle the |
| case of a linear regression with known error variance, something not |
| handled by \code{\link{summary.lm}}. |
| } |
| \value{ |
| \code{summary.glm} returns an object of class \code{"summary.glm"}, a |
| list with components |
| |
| \item{call}{the component from \code{object}.} |
| \item{family}{the component from \code{object}.} |
| \item{deviance}{the component from \code{object}.} |
| \item{contrasts}{the component from \code{object}.} |
| \item{df.residual}{the component from \code{object}.} |
| \item{null.deviance}{the component from \code{object}.} |
| \item{df.null}{the component from \code{object}.} |
| \item{deviance.resid}{the deviance residuals: |
| see \code{\link{residuals.glm}}.} |
| \item{coefficients}{the matrix of coefficients, standard errors, |
| z-values and p-values. Aliased coefficients are omitted.} |
| \item{aliased}{named logical vector showing if the original |
| coefficients are aliased.} |
| \item{dispersion}{either the supplied argument or the inferred/estimated |
| dispersion if the latter is \code{NULL}.} |
| \item{df}{a 3-vector of the rank of the model and the number of |
| residual degrees of freedom, plus number of coefficients (including |
| aliased ones).} |
| \item{cov.unscaled}{the unscaled (\code{dispersion = 1}) estimated covariance |
| matrix of the estimated coefficients.} |
| \item{cov.scaled}{ditto, scaled by \code{dispersion}.} |
| \item{correlation}{(only if \code{correlation} is true.) The estimated |
| correlations of the estimated coefficients.} |
| \item{symbolic.cor}{(only if \code{correlation} is true.) The value |
| of the argument \code{symbolic.cor}.} |
| } |
| \seealso{ |
| \code{\link{glm}}, \code{\link{summary}}. |
| } |
| \examples{ |
| ## For examples see example(glm) |
| } |
| \keyword{models} |
| \keyword{regression} |
| |