summary.cond: Summary Method for Objects of Class "cond"
In cond: Approximate Conditional Inference for Logistic and Loglinear Models

Description Usage Arguments Details Value Note See Also Examples

Returns a summary list for objects of class cond.

## 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, ...)

`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.

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.

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.

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

summary, cond.object

## 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)