predict.copreg: Prediction of the copula regression model In CopulaRegression: Bivariate Copula Based Regression Models

Description

This function predicts the outcome of a copula regression model for new data.

Usage

 ```1 2``` ```## S3 method for class 'copreg' predict(object,Rtest,Stest,exposure=rep(1,nrow(Stest)),independence=FALSE,...) ```

Arguments

 `object` `copreg` object returned from `copreg` `Rtest` design matrix of the new data for the Gamma model `Stest` design matrix of the new data for the zero truncated Poisson model `exposure` exposure time for the zero-truncated Poisson model, all entries of the vector have to be >0. Default is a constant vector of 1. `independence` logical. Do we assume that the two variables are independent. Default is FALSE. `...` other parameters

Details

For new data that is defined by the design matrices `Rtest` and `Stest`, and the exposure time `exposure`, the function predicts the values x of the Gamma variable, the values y of the (zero truncated) Poisson variable, and the policy loss. If `independence=TRUE`, the function predicts the policy loss under the assumption that X and Y are independent.

Value

 `x.pred` predicted value of x `y.pred` predicted value of y `l.pred` predicted value of the policy loss

Nicole Kraemer

References

N. Kraemer, E. Brechmann, D. Silvestrini, C. Czado (2013): Total loss estimation using copula-based regression models. Insurance: Mathematics and Economics 53 (3), 829 - 839.

`copreg`, `simulate_regression_data`

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```n<-200 # number of examples R<-S<-cbind(rep(1,n),rnorm(n)) # design matrices with intercept alpha<-beta<-c(1,-1) # regression coefficients exposure<-rep(1,n) # constant exposure delta<-0.5 # dispersion parameter tau<-0.3 # Kendall's tau family=3 # Clayton copula # simulate data my.data<-simulate_regression_data(n,alpha,beta,R,S,delta,tau,family,TRUE,exposure) x<-my.data[,1] y<-my.data[,2] # joint model without standard errors my.model<-copreg(x,y,R,S,family,exposure,FALSE,TRUE) # fitted values ## Not run: out<-predict(my.model,R,S) ```

Example output

```Loading required package: MASS