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)
## S3 method for class 'MCS':
plot(x, xlab="p", ylab="MCS", ...)
## S3 method for class 'bootMCS':
summary(object, alpha=0.05, ...)
## S3 method for class 'bootMCS':
plot(x, xlab="p", ylab="MCS", alpha=0.05, ylim, ...)

Arguments

A matrix of numeric variables.

The quantiles at which to evaluate.

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 (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.
MCSThe estimated correlations.
pThe quantiles at which the correlations were evaluated at
callThe function call used.
bootMCS returns an object of class bootMCS. There are plot and summary methods available for this class.
replicatesBootstrap replicates.
pThe quantiles at which the correlations were evaluated at
RNumber of bootstrap samples.
callThe 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

Examples

Run this code

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

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