CondCopulas (version 0.2.0)

se.estimated_CKT_kernel: Methods for class `estimated_CKT_kernel`

Description

Methods for class `estimated_CKT_kernel`

Usage

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

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.

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.

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)