VineCopula (version 1.3)

BiCopMetaContour: Contour plot of bivariate meta distribution with different margins and copula (theoretical and empirical)

Description

This function plots a bivariate contour plot corresponding to a bivariate meta distribution with different margins and specified bivariate copula and parameter values or creates corresponding empirical contour plots based on bivariate copula data.

Usage

BiCopMetaContour(u1=NULL, u2=NULL, bw=1, size=100,
                 levels=c(0.01,0.05,0.1,0.15,0.2), 
                 family="emp", par=0, par2=0, PLOT=TRUE,
                 margins="norm", margins.par=0, xylim=NA, ...)

Arguments

u1,u2

Data vectors of equal length with values in [0,1] (default: u1 and u2 = NULL).

bw

Bandwidth (smoothing factor; default: bw = 1).

size

Number of grid points; default: size = 100.

levels

Vector of contour levels. For Gaussian, Student t or exponential margins the default value (levels = c(0.01,0.05,0.1,0.15,0.2)) typically is a good choice. For uniform margins we recommend levels = c(0.1,0.3,0.5,0.7,0.9,1.1,1.

family

An integer defining the bivariate copula family or indicating an empirical contour plot: "emp" = empirical contour plot (default; margins can be specified by margins) 0 = independence copula 1 = Gauss

par

Copula parameter; if empirical contour plot, par = NULL or 0 (default).

par2

Second copula parameter for t-, BB1, BB6, BB7, BB8, Tawn type 1 and type 2 copulas (default: par2 = 0).

PLOT

Logical; whether the results are plotted. If PLOT = FALSE, the values x, y and z are returned (see below; default: PLOT = TRUE).

margins

Character; margins for the bivariate copula contour plot. Possible margins are: "norm" = standard normal margins (default) "t" = Student t margins with degrees of freedom as specified by margins.par "gamma"

margins.par

Parameter(s) of the distribution of the margins if necessary (default: margins.par = 0), i.e.,

• a positive real number for the degrees of freedom of Student t margins (seedt),
• a

xylim

A 2-dimensional vector of the x- and y-limits. By default (xylim = NA) standard limits for the selected margins are used.

...

Additional plot arguments.

Value

• x
  A vector of length size with the x-values of the kernel density estimator with Gaussian kernel if the empirical contour plot is chosen and a sequence of values in xylim if the theoretical contour plot is chosen.
• y
  A vector of length size with the y-values of the kernel density estimator with Gaussian kernel if the empirical contour plot is chosen and a sequence of values in xylim if the theoretical contour plot is chosen.
• z
  A matrix of dimension size with the values of the density of the meta distribution with chosen margins (see margins and margins.par) evaluated at the grid points given by x and y.

See Also

BiCopChiPlot, BiCopKPlot, BiCopLambda

Examples

## Example 1: contour plot of meta Gaussian copula distribution
## with Gaussian margins
tau = 0.5
fam = 1
theta = BiCopTau2Par(fam,tau)	
BiCopMetaContour(u1=NULL,u2=NULL,bw=1,size=100,
                 levels=c(0.01,0.05,0.1,0.15,0.2),
                 family=fam,par=theta,main="tau=0.5")


## Example 2: empirical contour plot with standard normal margins
dat = BiCopSim(N=1000,fam,theta)
BiCopMetaContour(dat[,1],dat[,2],bw=2,size=100,
                 levels=c(0.01,0.05,0.1,0.15,0.2),
                 par=0,family="emp",main="N=1000")

# empirical contour plot with exponential margins
BiCopMetaContour(dat[,1],dat[,2],bw=2,size=100,
                 levels=c(0.01,0.05,0.1,0.15,0.2),
                 par=0,family="emp",main="n=500",
                 margins="exp",margins.par=1)