residuals.sklarsomega: Extract model residuals.

In sklarsomega: Measuring Agreement Using Sklar's Omega Coefficient

Extract model residuals.

Description

Extract model residuals.

Usage

## S3 method for class 'sklarsomega'
residuals(object, ...)

Arguments

object	an object of class sklarsomega, typically the result of a call to sklars.omega.
...	additional arguments.

Details

Although residuals may not be terribly useful in this context, we provide residuals nonetheless. Said residuals are computed by first applying the probability integral transform, then applying the inverse probability integral transform, then pre-multiplying by the inverse of the square root of the (fitted) copula correlation matrix. For nominal or ordinal scores, the distributional transform approximation is used.

Value

A vector of residuals.

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.
# Produce a normal probability plot of the residuals, and overlay the line y = x.

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", control = list(dist = "laplace"))
summary(fit.lap)
res = residuals(fit.lap)
qqnorm(res, pch = 20)
abline(0, 1, col = "blue", lwd = 2)

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