These functions are all `methods`

for
class `vglm`

or
`summary.vglm`

objects.

```
summaryvglm(object, correlation = FALSE, dispersion = NULL,
digits = NULL, presid = FALSE,
HDEtest = TRUE, 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,
...)
# 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, ...)
```

object

an object of class `"vglm"`

, usually, a result of a
call to `vglm`

.

x

an object of class `"summary.vglm"`

, usually,
a result of a call to `summaryvglm()`

.

dispersion

used mainly for GLMs.
See `summary.glm`

.

correlation

logical; if `TRUE`

, the correlation matrix of
the estimated parameters is returned and printed.

digits

the number of significant digits to use when printing.

signif.stars

logical;
if `TRUE`

, ‘significance stars’
are printed for each coefficient.

presid

Pearson residuals; print out some summary statistics of these?

HDEtest

logical;
if `TRUE`

(the default) then a test for the HDE is performed,
else all arguments related to the HDE are ignored.

hde.NA

logical;
if a test for the Hauck-Donner effect is done
(for each coefficient)
and it is affirmative should that Wald test p-value be replaced by
an `NA`

?
The default is to do so.
Setting `hde.NA = FALSE`

will print the p-value even though
it will be biased upwards.
Also see argument `threshold.hde`

.

threshold.hde

numeric;
used if `hde.NA = TRUE`

and is present for some coefficients.
Only p-values greater than this argument will be replaced by
an `NA`

,
the reason being that small p-values will already be
statistically significant.
Hence setting `threshold.hde = 0`

will print out a `NA`

if the HDE is present.

quote

Fed into `print()`

.

nopredictors

logical;
if `TRUE`

the names of the linear predictors
are not printed out.
The default is that they are.

lrt0.arg, score0.arg, wald0.arg

Logical.
If `lrt0.arg = TRUE`

then the other
arguments are passed into `lrt.stat.vlm`

and the equivalent of the so-called Wald table is outputted.
Similarly,
if `score0.arg = TRUE`

then the other
arguments are passed into `score.stat.vlm`

and the equivalent of the so-called Wald table is outputted.
Similarly,
if `wald0.arg = TRUE`

then the other
arguments are passed into `wald.stat.vlm`

and the Wald table corresponding to that is outputted.
See details below.
Setting any of these will result in further IRLS iterations being
performed, therefore may be computationally expensive.

values0, subset, omit1s

These arguments are used if any of the
`lrt0.arg`

,
`score0.arg`

,
`wald0.arg`

arguments are used.
They are passed into the appropriate function,
such as `wald.stat.vlm`

.

top.half.only

logical; if `TRUE`

then only print out the top half
of the usual output.
Used for P-VGAMs.

prefix

Not used.

…

Not used.

`summaryvglm`

returns an object of class `"summary.vglm"`

;
see `summary.vglm-class`

.

Currently the SE column is deleted
when `lrt0 = TRUE`

because SEs are not so meaningful with the LRT.
In the future an SE column may be inserted (with `NA`

values)
so that it has 4-column output like the other tests.
In the meantime,
the columns of this matrix should be accessed by name and not number.

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`

,
`summary.glm`

,
`summary.lm`

,
`summary`

,
`hdeff.vglm`

,
`lrt.stat.vlm`

,
`score.stat`

,
`wald.stat`

.

```
# NOT RUN {
## For examples see example(glm)
pneumo <- transform(pneumo, let = log(exposure.time))
(afit <- vglm(cbind(normal, mild, severe) ~ let, acat, data = pneumo))
coef(afit, matrix = TRUE)
summary(afit) # Might suffer from the Hauck-Donner effect
coef(summary(afit))
summary(afit, lrt0 = TRUE, score0 = TRUE, wald0 = TRUE)
# }
```

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