summaryvglm {VGAM} | R Documentation

## 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 class '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.

### 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.

### Value

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

### See Also

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

### Examples

    ## 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))

[Package VGAM version 1.0-4 Index]