copulaFitPlot {cascsim}

Visualization Copula fitting

Description

Visualization Copula fitting

Usage

copulaFitPlot(object, ...)

## S4 method for signature 'CopulaObj'
copulaFitPlot(object)

Arguments

object

Copula Object

...

Additional parameters that may or may not be used

Examples

library(cascsim)
#Prepare pseudo observation data
library(copula)
dist1<-new("Pareto",p1=20,p2=3)
dist2<-new("Normal",p1=5,p2=3,min=0,max=20,truncated=TRUE)
dist3<-new("Lognormal",p1=2,p2=1,min=0,max=100,truncated=TRUE)
normal.cop <- normalCopula(c(0.6, 0.36, 0.6), dim=3, dispstr="un")
x <- rCopula(1000, normal.cop)
x[,1]<-Quantile(dist1,x[,1])
x[,2]<-Quantile(dist2,x[,2])
x[,3]<-Quantile(dist3,x[,3])
#Create Copula Object and Fit it to observation data without goodness of fit test
nom.cop <- new("CopulaObj", param=c(0.5,0.5,0.5),marginal=list(dist1=dist1,dist2=dist2,dist3=dist3),
dimension=3,observation=x,fittest=FALSE)
nom.cop <- copulaFit(nom.cop)
copulaFitPlot(nom.cop)
#Create Copula Object and Fit it to observation data with goodness of fit test
clayton.cop <- claytonCopula(c(3), dim=2)
x <- rCopula(1000, clayton.cop)
x[,1]<-Quantile(dist1,x[,1])
x[,2]<-Quantile(dist2,x[,2])
cla.cop <- new("CopulaObj", type="clayton",param=c(3),marginal=list(dist1=dist1,dist2=dist2),
dimension=2,observation=x,fittest=TRUE)
cla.cop <- copulaFit(cla.cop)
copulaFitPlot(cla.cop)