Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/predict.copreg.R
This function predicts the outcome of a copula regression model for new data.
1 2 |
object |
|
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 |
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.
x.pred |
predicted value of x |
y.pred |
predicted value of y |
l.pred |
predicted value of the policy loss |
Nicole Kraemer
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
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)
|
Loading required package: MASS
Loading required package: VineCopula
Warning messages:
1: In log(density_joint(x, y, mu, delta, lambda, theta, family, zt)) :
NaNs produced
2: In log(density_joint(x, y, mu, delta, lambda, theta, family, zt)) :
NaNs produced
3: In log(density_joint(x, y, mu, delta, lambda, theta, family, zt)) :
NaNs produced
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