Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/simulate_regression_data.R
Simulate regression data
1 | simulate_regression_data(n,alpha,beta,R,S,delta,tau,family,zt,exposure)
|
n |
number of samples |
alpha |
coefficients for the Gamma regression |
beta |
coefficients for the (zero-truncated) Poisson regression |
R |
n x p design matrix for the Gamma model |
S |
n x q design matrix for the (zero-truncated) Poisson model |
delta |
dispersion parameter of the Gamma distribution |
tau |
Kendalls tau |
family |
an integer defining the bivariate copula family: 1 = Gauss, 3 = Clayton, 4=Gumbel, 5=Frank |
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. |
zt |
logical. If |
We consider positive continuous random variables X_i and positive or non-negative count variables Y_i. We model X_i in terms of a covariate vector r_i and Y_i in terms of a covariate vector s_i. The marginal regression models are specified via
X_i\sim Gamma(μ_i,δ)
with \ln(μ_i)={ r_i}
^\top α
for the continuous variable. For the count variable, if zt=TRUE
, we use a zero-truncated Poisson model,
Y_i\sim ZTP(λ_{i})
with \ln(λ_{i})=\ln(e_i)+{s_i}^\top β. Otherwise, we use a Poisson model. e_i denotes the exposure time. Further,we assume that the dependency of X_i and Y_i is modeled in terms of a copula family with parameter θ.
n samples from the joint regression model
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.
1 2 3 4 5 6 7 8 9 10 11 | 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]
|
Loading required package: MASS
Loading required package: VineCopula
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.