VGAM (version 1.0-4)

summaryvglm: Summarizing Vector Generalized Linear Model Fits

Description

These functions are all methods for class vglm or summary.vglm objects.

Usage

summaryvglm(object, correlation = FALSE, dispersion = NULL,
            digits = NULL, presid = TRUE,
            hde.NA = TRUE, threshold.hde = 0.001,
            signif.stars = getOption("show.signif.stars"),
            nopredictors = FALSE, ...)
# S3 method for summary.vglm
show(x, digits = max(3L, getOption("digits") - 3L),
           quote = TRUE, prefix = "", presid = TRUE,
           hde.NA = TRUE, threshold.hde = 0.001,
           signif.stars = NULL, nopredictors = NULL,
           top.half.only = FALSE, ...)

Arguments

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?

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 biassed upwards.

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.

quote

Fed into print().

nopredictors

logical; if TRUE the names of the linear predictors are not printed out. The default is that they are.

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.

Value

summaryvglm returns an object of class "summary.vglm"; see summary.vglm-class.

Details

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.

The Hauck-Donner effect (HDE) is tested for some models; see hdeff.vglm for details. Arguments hde.NA and threshold.hde here are meant to give some control for the output for this aberration of the Wald statistic (so that the p-value is biassed upwards). If the HDE is present, using lrp.vglm is a good alternative as p-values based on the likelihood ratio test tend to be more accurate than Wald tests and do not suffer from the HDE.

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.

See Also

vglm, confintvglm, vcovvlm, summary.glm, summary.lm, summary, hdeff.vglm, lrp.vglm.

Examples

Run this code
# NOT RUN {
## 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 DataCamp Workspace