sklarsomega (version 3.0-2)

vcov.sklarsomega: Compute an estimated covariance matrix for a Sklar's Omega fit.

Description

Compute an estimated covariance matrix for a Sklar's Omega fit.

Usage

# S3 method for sklarsomega
vcov(object, ...)

Value

A matrix of estimated variances and covariances for the parameter estimator. This should have row and column names corresponding to the parameter names given by the coef method. Note that a call to this function will result in an error if sklars.omega was called with argument confint equal to "none", or if optimization failed.

Arguments

object

a fitted model object.

...

additional arguments.

Details

See the package vignette for detailed information regarding covariance estimation for Sklar's Omega.

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.

Examples

# Fit a subset of the cartilage data, assuming a Laplace marginal distribution. Compute
# confidence intervals in the usual ML way (observed information matrix). Also display
# the observed information matrix. Note that using confint = bootstrap leads to bootstrap
# sampling, in which case vcov returns the sample covariance matrix for the bootstrap
# sample.

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)
vcov(fit.lap)