HeckmanEM: Fit the Normal, Student-t or Contaminated normal Heckman...

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

Fit the Normal, Student-t or Contaminated normal Heckman Selection model

Description

'HeckmanEM()' fits the Heckman selection model.

Usage

HeckmanEM(
  y,
  x,
  w,
  cc,
  nu = 4,
  family = "T",
  error = 1e-05,
  iter.max = 500,
  im = TRUE,
  criteria = TRUE,
  verbose = TRUE
)

Arguments

y	A response vector.
x	A covariate matrix for the response y.
w	A covariate matrix for the missing indicator cc.
cc	A missing indicator vector (1=observed, 0=missing) .
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).
error	The absolute convergence error for the EM stopping rule.
iter.max	The maximum number of iterations for the EM algorithm.
im	TRUE/FALSE, boolean to decide if the standard errors of the parameters should be computed.
criteria	TRUE/FALSE, boolean to decide if the model selection criteria should be computed.
verbose	TRUE/FALSE, boolean to decide if the progress should be printed in the screen.

Value

An object of the class HeckmanEM with all the outputs provided from the function.

Examples

n    <- 100
nu   <- 3
cens <- 0.25

set.seed(13)
w <- cbind(1,runif(n,-1,1),rnorm(n))
x <- cbind(w[,1:2])
c <- qt(cens, df=nu)

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

set.seed(1)
datas <- rHeckman(x,w,beta,gamma,sigma2,rho = 0.6,nu,family="T")
y <- datas$y
cc <- datas$cc

# Normal EM
res.N <- HeckmanEM(y, x, w, cc, family="Normal",iter.max = 50)
# Student-t EM
res.T <- HeckmanEM(y, x, w, cc, nu = 4, family="T", iter.max = 50)

HeckmanEM documentation built on June 8, 2025, 10:45 a.m.