Summary Method for Objects of Class “cond”

Description

Returns a summary list for objects of class cond.

Usage

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## S3 method for class 'cond'
summary(object, alpha = 0.05, test = NULL, all = FALSE, coef = TRUE, 
        int = ifelse( (is.null(test) || all), TRUE, FALSE), 
        digits = NULL, ...)

Arguments

object

a cond object. This is assumed to be the result returned by the cond.glm function.

alpha

vector of levels for confidence intervals. The default is 5%.

test

vector of values of the parameter of interest one wants to test for. If NULL, no test is performed. The default is NULL.

all

logical value; if TRUE, all the information stored in the summary.cond object is printed, else only a subset of it. The default is FALSE.

coef

logical value; if TRUE, the unconditional and conditional parameter estimates are printed. The default is TRUE.

int

logical value; if TRUE confidence intervals are printed. The default is TRUE.

digits

number of significant digits to be printed. The default depends on the value of digits set by options.

...

absorbs any additional argument.

Details

This function is a method for the generic function summary() for objects of class cond. It can be invoked by calling summary or directly summary.cond for an object of the appropriate class.

Value

A list is returned with the following components.

coefficients

a 2x2 matrix containing the unconditional and approximate conditional MLEs and their standard errors.

conf.int

a matrix containing, for each level given in alpha, the upper and lower confidence bounds derived from several first- and higher order test statistics. One-sided and two-sided confidence intervals are considered. See cond.object for details on the test statistics.

signif.tests

a list with two elements. The first (stats) contains, for each value given in test, the values and tail probabilities of several first- and higher order test statistics. See cond.object for details on the test statistics.The second element of the list (qTerm) contains for each tested hypothesis the correction term used in the higher order solutions.

call

the function call that created the cond object.

formula

the model formula.

family

the variance function.

offset

the covariate occurring in the model formula whose coefficient represents the parameter of interest.

alpha

vector of levels used to compute the confidence intervals.

hypotheses

values for the parameter of interest that have been tested for.

diagnostics

information and nuisance parameters aspects; see cond.object for details.

n.approx

number of output points that have been calculated exactly.

all

logical value; if TRUE, all the information stored in the summary.cond object is printed.

cf

logical value; if TRUE, the unconditional and conditional parameter estimates are printed.

int

logical value; if TRUE, confidence intervals are printed.

is.scalar

a logical value indicating whether there are any nuisance parameters. If FALSE there are none.

digits

number of significant digits to be printed.

Note

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

See Also

summary, cond.object

Examples

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## Crying Babies Data
data(babies)
babies.glm <- glm(formula = cbind(r1, r2) ~ day + lull - 1, 
                  family = binomial, data = babies)
babies.cond <- cond(object = babies.glm, offset = lullyes)
summary(babies.cond, test = 0, coef = FALSE)