plot.estimated_CKT_kernel: Methods for class 'estimated_CKT_kernel'

In CondCopulas: Estimation and Inference for Conditional Copula Models

Methods for class 'estimated_CKT_kernel'

Description

Methods for class 'estimated_CKT_kernel'

Usage

## S3 method for class 'estimated_CKT_kernel'
se(object, progressBar = TRUE, ...)

## S3 method for class 'estimated_CKT_kernel'
confint(object, parm = NULL, level = 0.95, progressBar = TRUE, ...)

## S3 method for class 'estimated_CKT_kernel'
plot(
  x,
  confint = NULL,
  level = NULL,
  xlim = NULL,
  ylim = c(-1.2, 1.2),
  progressBar = TRUE,
  color_CKT = "black",
  color_confint = "red",
  ...
)

Arguments

object, x	an S3 object of class estimated_CKT_kernel.
progressBar	TRUE if a progress bar is plotted if computations of standard errors is needed. Note that in some case, the standard error is already available in the object itself, then no progress bar is needed.
...	other arguments, currently passed to plot.default only for the plot method. These are ignored for the other methods.
parm	ignored for the moment, kept for compatibility with the generic confint method.
level	the confidence level for the confidence intervals. By default, 95% confidence intervals are computed, i.e. level = 0.95.
confint	in case of the plot method, should confidence bands also be plotted?
xlim, ylim	the x,y limits of the plot.
color_CKT, color_confint	the colors respectively for the CKT curve and for the confidence intervals.

Value

plot is only called for its side effect and does not return anything.

se returns a vector of the same length as the number of points in the input newZ that was given to the function CKT.kernel.

confint returns a matrix with 2 columns and the same number of rows as the number of points in the input newZ that was given to the function CKT.kernel.

See Also

CKT.kernel which generates objects of class estimated_CKT_kernel.

Examples

# We simulate from a conditional copula
set.seed(1)
N = 100
# This is a small example for performance reasons.
# For a better example, use:
# N = 800
Z = rnorm(n = N, mean = 5, sd = 2)
conditionalTau = -0.9 + 1.8 * pnorm(Z, mean = 5, sd = 2)
simCopula = VineCopula::BiCopSim(N=N , family = 1,
    par = VineCopula::BiCopTau2Par(1 , conditionalTau ))
X1 = qnorm(simCopula[,1])
X2 = qnorm(simCopula[,2])

newZ = seq(2, 10, by = 1)
estimatedCKT_kernel <- CKT.kernel(
   X1 = X1, X2 = X2, Z = Z,
   newZ = newZ, h = 0.2, kernel.name = "Epa", se = TRUE)

se(estimatedCKT_kernel)
confint(estimatedCKT_kernel, level = 0.9)

plot(estimatedCKT_kernel, confint = TRUE)

CondCopulas documentation built on Nov. 24, 2025, 5:07 p.m.