summary.glm {stats}R Documentation

Summarizing Generalized Linear Model Fits


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


## 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"), ...)


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.


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

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

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

The dispersion is taken as 1 in the binomial and Poisson families, and otherwise estimated by the residual Chisquared statistic divided by the residual degrees of freedom.


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. Aliased coefficients are omitted.
aliased named logical vector showing if the original coefficients are aliased.
dispersion either the supplied argument or the estimated dispersion if the latter is NULL
df a 3-vector of the rank of the model and the number of residual degrees of freedom, plus number of non-aliased coefficients.
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.

See Also

glm, summary.


## --- Continuing the Example from  '?glm':


[Package stats version 2.2.1 Index]