HeckSelect: Title Regularization method for binary Heckman selection...

In EOgundimu300/HeckmanSelect: Regularization method for binary Heckman selection model

Description

Title Regularization method for binary Heckman selection model

Usage

HeckSelect(
  selection,
  outcome,
  data = sys.frame(sys.parent()),
  lambda = NULL,
  allowParallel = FALSE,
  penalty = c("LASSO", "ALASSO"),
  Model = c("Normal", "AMH"),
  crit = c("bic", "aic", "gcv"),
  ...
)

Arguments

selection	selection equation
outcome	outcome equation
data	data matrix containing both the outcome and selection variables
lambda	shrinkage parameter, both scalar and vector are acceptable When lambda=NULL, the internal vector of Lambda is used
allowParallel	If true, the "doParallel" package is invoked
penalty	can be ALASSO (for adaptive lasso) or LASSO (for Lasso) penalty
Model	can either be Normal error of AMH (Ali-Mikhail-Haq) copula function
crit	can be BIC, AIC or GCV, default is BIC
...

Value

class HeckSelect containing penalized coefficients and the parameters supplied for the creation of the object. Function call is also returned

Examples

HeckSelect(selection, outcome, data=data, allowParallel = TRUE, penalty="ALASSO", Model="AMH",crit="bic")

EOgundimu300/HeckmanSelect documentation built on Feb. 5, 2022, 2:48 a.m.