gkwreg (version 2.1.6)

confint.gkwreg: 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 gkwreg
confint(object, parm, level = 0.95, ...)

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).

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).

Author

Lopes, J. E.

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.

See Also

gkwreg, summary.gkwreg, confint

Examples

# \donttest{
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)
# }