plot.hac {HAC}

Plot of a HAC

Description

The function plots the structure of Hierarchical Archimedean Copulae.

Usage

 
## S3 method for class 'hac'
plot(x, xlim = NULL, ylim = NULL, xlab = "", ylab = "", 
col = "black", fg = "black", bg = "white", col.t = "black", lwd = 2, 
index = FALSE, numbering = FALSE, theta = TRUE, h = 0.4, l = 1.2, 
circles = 0.25, digits = 2, ...)

Arguments

x

a hac object. It can be constructed by hac or be the result of estimate.copula.

xlim, ylim

numeric vectors of length 2, giving the limits of the x and y axes. The default values adjust the size of the coordinate plane automatically with respect to the dimension of the HAC.

xlab, ylab

titles for the x and y axes.

col

defines the color of the lines, which connect the circles and rectangles.

fg

defines the color of the lines of the rectangles and circles equivalent to the color settings in R.

bg

defines the background color of the rectangles and circles equivalent to the color settings in R.

col.t

defines the text color equivalent to the color settings in R.

lwd

the width of the lines.

index

boolean. If index = TRUE, strings, which illustrate the subcopulae of the nodes, are used as subsrcipts of the dependency parameters.

numbering

boolean. If index = TRUE and numbering = TRUE, the dependency parameters are numbered. If x is returned by estimate.copula, the numbers correpsond to the estimation stages.

theta

boolean. Determines, whether the dependency parameter \theta or Kendall's rank correlation coefficient \tau is printed.

h

the height of the rectangles.

l

the width of the rectangles.

circles

a positive number giving the radius of the circles.

digits

an integer specifying the number of digits of the dependence parameter.

...

arguments to be passed to methods, e.g. graphical parameters (see par).

References

Okhrin, O. and Ristig, A. 2014, Hierarchical Archimedean Copulae: The ⁠HAC⁠ Package", Journal of Statistical Software, 58(4), 1-20, doi: 10.18637/jss.v058.i04.

See Also

estimate.copula

Examples

# a hac object is created

tree = list(list("X1", "X5", 3), list("X2", "X3", "X4", 4), 2)
model = hac(type = 1, tree = tree)
plot(model)

# the same procedure works for an estimated object

sample = rHAC(2000, model)
est.obj = estimate.copula(sample, epsilon = 0.2)
plot(est.obj)