methods for class vglm or
summary.vglm objects.summaryvglm(object, correlation = FALSE,
dispersion = NULL, digits = NULL, presid = TRUE,
signif.stars = getOption("show.signif.stars"),
nopredictors = FALSE, ...)
## S3 method for class 'summary.vglm':
show(x, digits = max(3L, getOption("digits") - 3L),
quote = TRUE, prefix = "", presid = TRUE,
signif.stars = NULL, nopredictors = NULL, ...)"vglm", usually, a result of a
call to vglm."summary.vglm", usually, a result of a
call to summaryvglm().summary.glm.TRUE, the correlation matrix of
the estimated parameters is returned and printed.TRUE, print().TRUE the names of the linear predictors
are not printed out.
The default is that they are.summaryvglm returns an object of class "summary.vglm";
see summary.vglm-class.show.summary.vglm() 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 z value regardless of
whether the
dispersion is estimated or known
(or fixed by the family). A fourth column gives the two-tailed
p-value corresponding to the z ratio based on a
Normal reference distribution.In general, the t distribution is not used, but the normal
distribution is used.
Correlations are printed to two decimal places (or symbolically): to
see the actual correlations print summary(object)@correlation
directly. It is possible for programmers to write a methods function to
print out extra quantities when summary(vglmObject) is
called.
The generic function is summaryvglmS4VGAM(), and one
can use the S4 function setMethod to
compute the quantities needed.
Also needed is the generic function is showsummaryvglmS4VGAM()
to actually print the quantities out.vglm,
confintvglm,
vcovvlm,
summary.glm,
summary.lm,
summary.## For examples see example(glm)
pneumo <- transform(pneumo, let = log(exposure.time))
(fit <- vglm(cbind(normal, mild, severe) ~ let, acat, data = pneumo))
coef(fit, matrix = TRUE)
summary(fit)
coef(summary(fit))Run the code above in your browser using DataLab