confint.univariateML: Confidence Intervals for Maximum Likelihood Estimates

In univariateML: Maximum Likelihood Estimation for Univariate Densities

Confidence Intervals for Maximum Likelihood Estimates

Description

Computes a confidence interval for one or more parameters in a unvariateML object.

Usage

## S3 method for class 'univariateML'
confint(object, parm = NULL, level = 0.95, Nreps = 1000, ...)

Arguments

object	An object of class univariateML.
parm	Vector of strings; the parameters to calculate a confidence interval for. Each parameter must be a member of names(object).
level	The confidence level.
Nreps	Number of bootstrap iterations. Passed to bootstrapml().
...	Additional arguments passed to bootstrapml().

Details

confint.univariateML is a wrapper for bootstrapml() that computes confidence intervals for the main parameters of object. The main parameters of object are the members of names(object). For instance, the main parameters of an object obtained from mlnorm are mean and sd. The confidence intervals are parametric bootstrap percentile intervals with limits (1-level)/2 and 1 - (1-level).

Value

A matrix or vector with columns giving lower and upper confidence limits for each parameter in parm.

See Also

stats::confint() for the generic function and bootstrapml() for the function used to calculate the confidence intervals.

Examples

object <- mlinvgauss(airquality$Wind)
confint(object) # 95% confidence interval for mean and shape
confint(object, "mean") # 95% confidence interval for the mean parameter
# confint(object, "variance") # Fails since 'variance isn't a main parameter.

univariateML documentation built on April 3, 2025, 11:09 p.m.