summaryvglm {VGAM} | R Documentation |
These functions are all 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, ...)
object |
an object of class |
x |
an object of class |
dispersion |
used mainly for GLMs.
See |
correlation |
logical; if |
digits |
the number of significant digits to use when printing. |
signif.stars |
logical; if |
presid |
Pearson residuals; print out some summary statistics of these? |
quote |
Fed into |
nopredictors |
logical;
if |
prefix |
Not used. |
... |
Not used. |
show.summary.vglm()
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 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.
summaryvglm
returns an object of class "summary.vglm"
;
see summary.vglm-class
.
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))