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))
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