These functions are all methods for
class vglm or
summary.vglm objects.
summaryvglm(object, correlation = FALSE, dispersion = NULL,
digits = NULL, presid = FALSE,
HDEtest = FALSE, hde.NA = TRUE, threshold.hde = 0.001,
signif.stars = getOption("show.signif.stars"),
nopredictors = FALSE,
lrt0.arg = FALSE, score0.arg = FALSE, wald0.arg = FALSE,
values0 = 0, subset = NULL, omit1s = TRUE,
wsdm.arg = FALSE, hdiff = 0.005,
retry = TRUE, mux.hdiff = 1, eps.wsdm = 0.15,
Mux.div = 3, doffset.wsdm = NULL, ...)
# S3 method for summary.vglm
show(x, digits = max(3L, getOption("digits") - 3L),
quote = TRUE, prefix = "", presid = length(x@pearson.resid) > 0,
HDEtest = TRUE, hde.NA = TRUE, threshold.hde = 0.001,
signif.stars = NULL, nopredictors = NULL,
top.half.only = FALSE, ...)summaryvglm returns an object of class "summary.vglm";
see summary.vglm-class.
Originally, summaryvglm() was written to be
very similar to summary.glm,
however now there are a quite a few more options available.
By default,
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.
Correlations are printed to two decimal places (or symbolically): to
see the actual correlations print summary(object)@correlation
directly.
The Hauck-Donner effect (HDE) is tested for almost all models;
see hdeff.vglm for details.
Arguments hde.NA and threshold.hde here are meant
to give some control of the output if this aberration of the
Wald statistic occurs (so that the p-value is biased upwards).
If the HDE is present then using lrt.stat.vlm
to get a more accurate p-value is a good
alternative as p-values based on the likelihood ratio test (LRT)
tend to be more accurate than Wald tests and do not suffer
from the HDE.
Alternatively, if the HDE is present
then using wald0.arg = TRUE
will compute Wald statistics that are HDE-free; see
wald.stat.
The arguments lrt0.arg and score0.arg
enable the so-called Wald table to be replaced by
the equivalent LRT and Rao score test table;
see
lrt.stat.vlm,
score.stat.
Further IRLS iterations are performed for both of these,
hence the computational cost might be significant.
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,
wsdm,
summary.rrvglm,
summary.glm,
summary.lm,
summary,
hdeff.vglm,
lrt.stat.vlm,
score.stat,
wald.stat.
## For examples see example(glm)
pneumo <- transform(pneumo, let = log(exposure.time))
(afit <- vglm(cbind(normal, mild, severe) ~ let, acat, pneumo))
coef(afit, matrix = TRUE)
summary(afit) # Might suffer from the HDE?
coef(summary(afit))
summary(afit, lrt0 = TRUE, score0 = TRUE, wald0 = TRUE)
summary(afit, HDEtest = TRUE)
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