summary.glm
Summarizing Generalized Linear Model Fits
These functions are all methods for class glm or
summary.glm objects.
- Keywords
- models, regression
Usage
# S3 method for glm
summary(object, dispersion = NULL, correlation = FALSE,
symbolic.cor = FALSE, …)# S3 method for summary.glm
print(x, digits = max(3, getOption("digits") - 3),
symbolic.cor = x$symbolic.cor,
signif.stars = getOption("show.signif.stars"), …)
Arguments
- object
an object of class
"glm", usually, a result of a call toglm.- x
an object of class
"summary.glm", usually, a result of a call tosummary.glm.- dispersion
the dispersion parameter for the family used. Either a single numerical value or
NULL(the default), when it is inferred fromobject(see ‘Details’).- correlation
logical; if
TRUE, the correlation matrix of the estimated parameters is returned and printed.- digits
the number of significant digits to use when printing.
- symbolic.cor
logical. If
TRUE, print the correlations in a symbolic form (seesymnum) rather than as numbers.- signif.stars
logical. If
TRUE, ‘significance stars’ are printed for each coefficient.- …
further arguments passed to or from other methods.
Details
print.summary.glm tries to be smart about formatting the
coefficients, standard errors, etc. and additionally gives
‘significance stars’ if signif.stars is TRUE.
The coefficients component of the result gives the estimated
coefficients and their estimated standard errors, together with their
ratio. This third column is labelled t ratio if the
dispersion is estimated, and z ratio if the dispersion is known
(or fixed by the family). A fourth column gives the two-tailed
p-value corresponding to the t or z ratio based on a Student t or
Normal reference distribution. (It is possible that the dispersion is
not known and there are no residual degrees of freedom from which to
estimate it. In that case the estimate is NaN.)
Aliased coefficients are omitted in the returned object but restored
by the print method.
Correlations are printed to two decimal places (or symbolically): to
see the actual correlations print summary(object)$correlation
directly.
The dispersion of a GLM is not used in the fitting process, but it is
needed to find standard errors.
If dispersion is not supplied or NULL,
the dispersion is taken as 1 for the binomial and
Poisson families, and otherwise estimated by the residual
Chisquared statistic (calculated from cases with non-zero weights)
divided by the residual degrees of freedom.
summary can be used with Gaussian glm fits to handle the
case of a linear regression with known error variance, something not
handled by summary.lm.
Value
summary.glm returns an object of class "summary.glm", a
list with components
the component from object.
the component from object.
the component from object.
the component from object.
the component from object.
the component from object.
the component from object.
the deviance residuals:
see residuals.glm.
the matrix of coefficients, standard errors, z-values and p-values. Aliased coefficients are omitted.
named logical vector showing if the original coefficients are aliased.
either the supplied argument or the inferred/estimated
dispersion if the latter is NULL.
a 3-vector of the rank of the model and the number of residual degrees of freedom, plus number of coefficients (including aliased ones).
the unscaled (dispersion = 1) estimated covariance
matrix of the estimated coefficients.
ditto, scaled by dispersion.
(only if correlation is true.) The estimated
correlations of the estimated coefficients.
(only if correlation is true.) The value
of the argument symbolic.cor.
See Also
Examples
library(stats)
# NOT RUN {
## For examples see example(glm)
# }