summary.glm
Summarizing Generalized Linear Model Fits
These functions are all methods
for class glm
or
summary.glm
objects.
- Keywords
- models, regression
Usage
"summary"(object, dispersion = NULL, correlation = FALSE, symbolic.cor = FALSE, ...)
"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
- 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 inferred/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 coefficients (including aliased ones).
- 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 argumentsymbolic.cor
.
summary.glm
returns an object of class "summary.glm"
, a
list with componentsSee Also
Examples
library(stats)
## For examples see example(glm)