confint.sklarsomega: Compute confidence/credible intervals for Sklar's Omega.

In sklarsomega: Measuring Agreement Using Sklar's Omega Coefficient

Compute confidence/credible intervals for Sklar's Omega.

Description

Compute confidence/credible intervals for Sklar's Omega.

Usage

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

Arguments

object	an object of class "sklarsomega", the result of a call to sklars.omega.
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 desired confidence level for the interval. The default value is 0.95.
...	additional arguments. These are passed to quantile if confint is equal to "bootstrap".

Details

This function computes confidence/credible intervals for a Sklar's Omega fit.

Value

A vector with entries giving lower and upper confidence limits. These will be labelled as (1 - level) / 2 and 1 - (1 - level) / 2.

References

Nissi, M. J., Mortazavi, S., Hughes, J., Morgan, P., and Ellermann, J. (2015). T2* relaxation time of acetabular and femoral cartilage with and without intra-articular Gd-DTPA2 in patients with femoroacetabular impingement. American Journal of Roentgenology, 204(6), W695.

See Also

sklars.omega

Examples

# Fit a subset of the cartilage data, assuming a Laplace marginal distribution. Compute
# confidence intervals in the usual ML way (observed information matrix). Note that
# calling function sklars.omega with confint = bootstrap will lead to bootstrap sampling,
# in which case confidence intervals will be bootstrap intervals.

data(cartilage)
data.cart = as.matrix(cartilage)[1:100, ]
colnames(data.cart) = c("c.1.1", "c.2.1")
fit.lap = sklars.omega(data.cart, level = "balance", confint = "asymptotic",
                       control = list(dist = "laplace"))
summary(fit.lap)
confint(fit.lap, level = 0.99)

sklarsomega documentation built on April 4, 2023, 5:15 p.m.