methods for class wle.glm or
summary.wle.glm objects.
"summary"(object, root = 1, dispersion = NULL, correlation = FALSE, symbolic.cor = FALSE, ...)
"print"(x, digits = max(3, getOption("digits") - 3), symbolic.cor = x$symbolic.cor, signif.stars = getOption("show.signif.stars"), ...)"wle.glm", usually, a result of a
call to wle.glm."summary.wle.glm", usually, a result of a
call to summary.glm.NULL (the default), when
it is inferred from object (see ‘Details’).TRUE, the correlation matrix of
the estimated parameters is returned and printed.TRUE, print the correlations in
a symbolic form (see symnum) rather than as numbers.TRUE, ‘significance stars’
are printed for each coefficient.summary.wle.glm returns an object of class "summary.wle.glm", a
list with componentsobject.object.object.object.object.object.object.residuals.glm.NULL.dispersion = 1) estimated covariance
matrix of the estimated coefficients.dispersion.correlation is true.) The estimated
correlations of the estimated coefficients.correlation is true.) The value
of the argument symbolic.cor.print.summary.wle.glm tries to be smart about formatting the
coefficients, standard errors, etc. and additionally gives
‘significance stars’ if signif.stars is TRUE.
The coefficients component of the result gives the estimated
coefficients and their estimated standard errors, together with their
ratio. This third column is labelled t ratio if the
dispersion is estimated, and z ratio if the dispersion is known
(or fixed by the family). A fourth column gives the two-tailed
p-value corresponding to the t or z ratio based on a Student t or
Normal reference distribution. (It is possible that the dispersion is
not known and there are no residual degrees of freedom from which to
estimate it. In that case the estimate is NaN.) Aliased coefficients are omitted in the returned object but restored
by the print method.
Correlations are printed to two decimal places (or symbolically): to
see the actual correlations print summary(object)$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 wle.glm fits to handle the
case of a linear regression with known error variance, something not
handled by summary.wle.lm.
wle.glm, summary.
## --- Continuing the Example from '?wle.glm':
summary(wle.glm.D93)
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