# copulaFit-methods: Copula fitting In cascsim: Casualty Actuarial Society Individual Claim Simulator

Copula fitting

## Usage

 ```1 2 3 4``` ```copulaFit(object, ...) ## S4 method for signature 'CopulaObj' copulaFit(object) ```

## Arguments

 `object` Copula Object `...` Additional parameters that may or may not be used

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```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) nom.cop@coutput #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) cla.cop@coutput ```

cascsim documentation built on Jan. 13, 2020, 5:07 p.m.