R: Summarizing Generalized Linear Model Fits

glm.summary {base}

R Documentation

Summarizing Generalized Linear Model Fits

Description

These functions are all methods for class glm or summary.glm objects.

Usage

## 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"), ...)

Arguments

`object`	an object of class `"glm"`, usually, a result of a call to `glm`.
`x`	an object of class `"summary.glm"`, usually, a result of a call to `summary.glm`.
`dispersion`	the dispersion parameter for the fitting family. By default it is obtained from `object`.
`correlation`	logical; if `TRUE`, the correlation matrix of the estimated parameters is returned and printed.
`digits`	the number of significant digits to use when printing.
`symbolic.cor`	logical. If `TRUE`, print the correlations in a symbolic form (see `symnum`) rather than as numbers.
`signif.stars`	logical. If `TRUE`, “significance stars” are printed for each coefficient.
`...`	further arguments passed to or from other methods.

Details

print.summary.glm tries to be smart about formatting the coefficients, standard errors, etc. and additionally gives “significance stars” if signif.stars is TRUE.

Correlations are printed to two decimal places (or symbolically): to see the actual correlations print summary(object)$correlation directly.

Value

summary.glm returns an object of class "summary.glm", a list with components

`call`	the component from `object`.
`family`	the component from `object`.
`deviance`	the component from `object`.
`contrasts`	the component from `object`.
`df.residual`	the component from `object`.
`null.deviance`	the component from `object`.
`df.null`	the component from `object`.
`deviance.resid`	the deviance residuals: see `residuals.glm`.
`coefficients`	the matrix of coefficients, standard errors, z-values and p-values.
`dispersion`	eother the supplied argument or the estimated dispersion if the latter in `NULL`
`df`	a 2-vector of the rank of the model and the number of residual degrees of freedom.
`cov.unscaled`	the unscaled (`dispersion = 1`) estimated covariance matrix of the estimated coefficients.
`cov.scaled`	ditto, scaled by `dispersion`.
`correlation`	(only if `correlation` is true.) The estimated correlations of the estimated coefficients.
`symbolic.cor`	(only if `correlation` is true.) The value of the argument `symbolic.cor`.

Examples

## --- Continuing the Example from  ``?glm'':%\code{\link{glm}}:

summary(glm.D93)

[Package base version 1.7.1 ]