confint.logbin: Confidence Intervals for logbin Model Parameters

In mdonoghoe/logbin: Relative Risk Regression Using the Log-Binomial Model

Confidence Intervals for logbin Model Parameters

Description

Computes confidence intervals for one or more parameters in a fitted logbin model.

Usage

## S3 method for class 'logbin'
confint(object, parm, level = 0.95, ...)

Arguments

object	a fitted model object, resulting from a call to logbin.
parm	a specification of which parameters are to be given confidence intervals, either a vector of numbers or a vector of names. If missing, all parameters are considered.
level	the confidence level required.
...	additional argument(s) passed to confint.default.

Details

Calculates confidence intervals for model parameters assuming asymptotic normality and using the result from vcov.logbin(object). As such, if the MLE is on the boundary of the parameter space, (as per object$boundary) the normality assumption is invalid and NA is returned.

Value

A matrix (or vector) with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1-level)/2 and 1-(1-level)/2 in % (by default 2.5% and 97.5%).

Author(s)

Mark W. Donoghoe [e-mail]

See Also

confint.default, vcov.logbin

Examples

## For an example, see example(logbin)

mdonoghoe/logbin documentation built on June 15, 2025, 5:29 a.m.