hoa: print.summary.cond – R documentation

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print.summary.cond

Use print() on a “summary.cond” object

Description

This is a method for the function print() for objects inheriting from class summary.cond. See print and print.default for the general behaviour of this function and for the interpretation of digits.

Usage

## S3 method for class 'summary.cond'
print(x, all = x$all, Coef = x$cf, int = x$int, test = x$hyp,
      digits = if(!is.null(x$digits)) x$digits else max(3, getOption("digits")-3), 
      ...)
## S3 method for class 'summary.cond'
print(x, all, Coef, int, test, digits, ...)

Arguments

`x`	a `summary.cond` object. This is assumed to be the result returned by the `summary.cond` function.
`all`	if `TRUE` all the information stored in the `summary.cond` object is printed, else only a subset of it. The default is `FALSE`.
`Coef`	if `TRUE`, the unconditional and conditional parameter estimates are printed. The default is `TRUE`.
`int`	if `TRUE`, confidence intervals are printed. The default is `TRUE`.
`test`	if `TRUE`, tests statistics and tail probabilities are printed. The default is `FALSE`.
`digits`	number of significant digits to be printed. The default depends on the value of `digits` set by `options`.
`...`	additional arguments.

Details

Changing the default values of all, Coef, int and test allows only a subset of the information in the summary.cond object to be printed. With all = FALSE, one-sided confidence intervals and the Lugannani-Rice tail approximations are omitted. See summary.cond for more details.

Note

The amount of information printed may vary depending on whether there are any nuisance parameters.

Examples

## Urine Data 
data(urine)
urine.glm <- glm(r ~ gravity + ph + osmo + conduct + urea + calc, 
                 family = binomial, data = urine)
urine.cond <- cond(urine.glm, urea)
print(summary(urine.cond, all = TRUE), digits = 4)
print(summary(urine.cond), Coef = FALSE)

hoa

Higher Order Likelihood Inference

v2.1.4.1

GPL (>= 2) | file LICENCE

Authors

R port by Alessandra R. Brazzale <alessandra.brazzale@unipd.it>, following earlier work by Douglas Bates. The function tem is based on work by Anthony Davison <Anthony.Davison@epfl.ch>

Initial release

2015-08-11