texmex (version 2.1)

MCS: Multivariate conditional Spearman's rho

Description

Compute multivariate conditional Spearman's rho over a range of quantiles.

Usage

MCS(X, p = seq(0.1, 0.9, by = 0.1))
bootMCS(X, p = seq(0.1, 0.9, by = 0.1), R = 100, trace = 10)
"plot"(x, xlab="p", ylab="MCS", ...)
"summary"(object, alpha=0.05, ...)
"plot"(x, xlab="p", ylab="MCS", alpha=0.05, ylim, ...)

Arguments

X

A matrix of numeric variables.

p

The quantiles at which to evaluate.

R

The number of bootstrap samples to run. Defaults to R = 100.

trace

How often to inform the user of progress. Defaults to trace = 10.

x, object

An object of class MCS or bootMCS.

xlab, ylab

Axis labels.

alpha

A 100(1 - alpha)% pointwise confidence interval will be produced. Defaults to alpha = 0.05.

ylim

Plotting limits for bootstrap plot.

...

Optional arguments to be passed into methods.

Value

MCS returns an object of class MCS. There are plot and summary methods available for this class.

MCS

The estimated correlations.

p

The quantiles at which the correlations were evaluated at

call

The function call used.

bootMCS returns an object of class bootMCS. There are plot and summary methods available for this class.

replicates

Bootstrap replicates.

p

The quantiles at which the correlations were evaluated at

R

Number of bootstrap samples.

call

The function call used.

Details

The method is described in detail by Schmid and Schmidt (2007). The main code was written by Yiannis Papastathopoulos, wrappers written by Harry Southworth. When the result of a call to bootMCS is plotted, simple quantile bootstrap confidence intervals are displayed.

References

F. Schmid and R. Schmidt, Multivariate conditional versions of Spearman's rho and related measures of tail dependence, Journal of Multivariate Analysis, 98, 1123 -- 1140, 2007

See Also

chi

Examples

D <- liver[liver$dose == "D",]
plot(D)
# Following lines commented out to keep CRAN happy
#Dmcs <- bootMCS(D[, 5:6])
#Dmcs
#plot(Dmcs)