sumry.glm: Summarizing Generalized Linear Model Fits

Description

These functions are methods for class glm or sumry.glm objects.

Usage

# S3 method for glm
sumry(object, dispersion = NULL, correlation = FALSE,
        symbolic.cor = FALSE, ...)
# S3 method for sumry.glm
print(x, digits = max(3L, getOption("digits") - 3L), 
                          symbolic.cor = FALSE, 
                          signif.stars = getOption("show.signif.stars"), 
                          call=FALSE, deviance.residuals=FALSE, 
                          show.iter=10, ...)

Value

sumry.glm returns an object of class "sumry.glm", a list with the same components as summary.glm.

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 family used. Either a single numerical value or NULL (the default), when it is inferred from object (see ‘Details’).
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.
call: logical. If TRUE, details of the function call are printed.
deviance.residuals: logical. If TRUE, deviance residuals are printed.
show.iter: NULL or integer. If NULL, or if the number of iterations is greater than the specified integer, then the number of iterations will be printed.
...: further arguments passed to or from other methods.

Details

The function print.sumry.glm allows, relative to print.summary.glm, some greater flexibility in what is printed. By default, details of the call to glm are omitted, and details of the number of interations only in the unusual case where this number is greater than 10. See the help page for summary.glm for further details.

Examples

Run this code

## For examples see example(glm)