# 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 class 'glm':
summary(object, dispersion = NULL, correlation = FALSE,
symbolic.cor = FALSE, \dots)
```## 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 family used.
Either a single numerical value or
`NULL`

(the default), when it is inferred from`object`

(seeDetails ). - 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
`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 componentscall 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 argument`symbolic.cor`

.

##### See Also

##### Examples

`library(stats)`

`## For examples see example(glm)`

*Documentation reproduced from package stats, version 3.3, License: Part of R 3.3*