rHeckman: Data generation from the Heckman Selection model (Normal,...

In HeckmanEM: Fit Normal, Student-t or Contaminated Normal Heckman Selection Models

Data generation from the Heckman Selection model (Normal, Student-t or CN)

Description

'rHeckman()' generates a random sample from the Heckman selection model (Normal, Student-t or CN).

Usage

rHeckman(x, w, beta, gamma, sigma2, rho, nu = 4, family = "T")

Arguments

x	A covariate matrix for the response y.
w	A covariate matrix for the missing indicator cc.
beta	Values for the beta vector.
gamma	Values for the gamma vector.
sigma2	Value for the variance.
rho	Value for the dependence between the response and missing value.
nu	When using the t- distribution, the initial value for the degrees of freedom. When using the CN distribution, the initial values for the proportion of bad observations and the degree of contamination.
family	The family to be used (Normal, T, or CN).

Value

Return an object with the response (y) and missing values (cc).

Examples

n <- 100
rho <- .6
cens <- 0.25
nu <- 4
set.seed(20200527)
w <- cbind(1,runif(n,-1,1),rnorm(n))
x <- cbind(w[,1:2])

family <- "T"
c <- qt(cens, df=nu)

sigma2 <- 1
beta <- c(1,0.5)
gamma<- c(1,0.3,-.5)
gamma[1] <- -c*sqrt(sigma2)

data <- rHeckman(x,w,beta,gamma,sigma2,rho,nu,family=family)

HeckmanEM documentation built on May 29, 2024, 8:41 a.m.