HeckmanEM (version 0.2-1)

HeckmanEM: 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
)

Value

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

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.

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
# \donttest{
# 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)
# }