Description Usage Arguments Value Note Author(s) See Also Examples
View source: R/BiCopMetaContour.r
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.
1 2 3 4 
u1,u2 
Data vectors of equal length with values in [0,1] (default: 
bw 
Bandwidth (smoothing factor; default: 
size 
Number of grid points; default: 
levels 
Vector of contour levels.
For Gaussian, Student t or exponential margins the default value ( 
family 
An integer defining the bivariate copula family or indicating an empirical contour plot: 
par 
Copula parameter; if empirical contour plot, 
par2 
Second copula parameter for t, BB1, BB6, BB7 and BB8 copulas (default: 
PLOT 
Logical; whether the results are plotted.
If 
margins 
Character; margins for the bivariate copula contour plot. Possible margins are: 
margins.par 
Parameter(s) of the distribution of the margins if necessary (default:

xylim 
A 2dimensional vector of the x and ylimits.
By default ( 
... 
Additional plot arguments. 
x 
A vector of length 
y 
A vector of length 
z 
A matrix of dimension 
Warning: The combination family = 0
(independence copula) and margins = "unif"
(uniform margins) is not possible because all z
values are equal.
Ulf Schepsmeier, Alexander Bauer
BiCopChiPlot
, BiCopKPlot
, BiCopLambda
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  ## 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)

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