confint.gkwreg: Confidence Intervals for Generalized Kumaraswamy Regression...

In gkwreg: Generalized Kumaraswamy Regression Models for Bounded Data

Confidence Intervals for Generalized Kumaraswamy Regression Parameters

Description

Computes confidence intervals for model parameters in fitted gkwreg objects using Wald (normal approximation) method based on asymptotic theory.

Usage

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

Arguments

object	An object of class "gkwreg" from gkwreg.
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. Default is 0.95.
...	Additional arguments (currently unused).

Details

The confidence intervals are computed using the Wald method based on asymptotic normality of maximum likelihood estimators:

CI = \hat{\theta} \pm z_{\alpha/2} \times SE(\hat{\theta})

where z_{\alpha/2} is the appropriate normal quantile and SE(\hat{\theta}) is the standard error from the Hessian matrix.

The model must have been fitted with hessian = TRUE (the default) in gkw_control. If standard errors are not available, an error is raised.

Value

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

Author(s)

Lopes, J. E.

See Also

gkwreg, summary.gkwreg, confint

Examples

data(GasolineYield)
fit <- gkwreg(yield ~ batch + temp, data = GasolineYield, family = "kw")

# 95 percent confidence intervals
confint(fit)

# 90 percent confidence intervals
confint(fit, level = 0.90)

# Specific parameters
confint(fit, parm = "alpha:(Intercept)")
confint(fit, parm = 1:3)

gkwreg documentation built on Nov. 27, 2025, 5:06 p.m.