Compute multivariate conditional Spearman's rho over a range of quantiles.
MCS(X, p = seq(0.1, 0.9, by = 0.1))# S3 method for MCS
plot(x, xlab = "p", ylab = "MCS", ...)
# S3 method for MCS
ggplot(data, mapping, main = "", ..., environment)
bootMCS(X, p = seq(0.1, 0.9, by = 0.1), R = 100, trace = 10)
# S3 method for bootMCS
ggplot(data, mapping, main = "", alpha = 0.05, ylim, ..., environment)
# S3 method for bootMCS
plot(x, xlab = "p", ylab = "MCS", alpha = 0.05, ylim, ...)
# S3 method for bootMCS
summary(object, alpha = 0.05, ...)
# S3 method for summary.bootMCS
print(x, ...)
MCS returns an object of class MCS. There are plot and
print methods available for this class.
The estimated correlations.
The quantiles at which the correlations were evaluated at
The function call used.
bootMCS returns an object of class bootMCS. There are plot and
summary methods available for this class.
Bootstrap replicates.
The quantiles at which the correlations were evaluated at
Number of bootstrap samples.
The function call used.
A matrix of numeric variables.
The quantiles at which to evaluate.
An object of class MCS or bootMCS.
Axis labels.
Optional arguments to be passed into methods.
Arguments to ggplot method.
The number of bootstrap samples to run. Defaults to R = 100.
How often to inform the user of progress. Defaults to
trace = 10.
A 100(1 - alpha)% pointwise confidence interval will be
produced. Defaults to alpha = 0.05.
Plotting limits for bootstrap plot.
Yiannis Papastathopoulos, Harry Southworth
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.
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
chi
D <- liver[liver$dose == "D",]
plot(D)
# \donttest{
Dmcs <- bootMCS(D[, 5:6])
Dmcs
plot(Dmcs)
# }
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