# summary.glm

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##### 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$symbolic.cor, signif.stars = getOption("show.signif.stars"), …) ##### Arguments object an object of class "glm", usually, a result of a call to glm. x an object of class "summary.glm", usually, a result of a call to summary.glm. dispersion the dispersion parameter for the family used. Either a single numerical value or NULL (the default), when it is inferred from object (see ‘Details’). correlation logical; if TRUE, the correlation matrix of the estimated parameters is returned and printed. digits the number of significant digits to use when printing. symbolic.cor logical. If TRUE, print the correlations in a symbolic form (see symnum) rather than as numbers. signif.stars logical. If TRUE, ‘significance stars’ are printed for each coefficient. further arguments passed to or from other methods. ##### Details print.summary.glm tries to be smart about formatting the coefficients, standard errors, etc. and additionally gives ‘significance stars’ if signif.stars is TRUE. The coefficients component of the result gives the estimated coefficients and their estimated standard errors, together with their ratio. This third column is labelled t ratio if the dispersion is estimated, and z ratio if the dispersion is known (or fixed by the family). A fourth column gives the two-tailed p-value corresponding to the t or z ratio based on a Student t or Normal reference distribution. (It is possible that the dispersion is not known and there are no residual degrees of freedom from which to estimate it. In that case the estimate is NaN.) Aliased coefficients are omitted in the returned object but restored by the print method. Correlations are printed to two decimal places (or symbolically): to see the actual correlations print summary(object)$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

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 argument symbolic.cor.

glm, summary.
library(stats) # NOT RUN { ## For examples see example(glm) # }