% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hdCBPS.R
\name{hdCBPS}
\alias{hdCBPS}
\alias{hdCBPS.fit}
\title{hdCBPS high dimensional CBPS method to parses the formula object and passes the result to hdCBPS.fit, which calculates ATE using CBPS method in a high dimensional setting.}
\usage{
hdCBPS(formula, data, na.action, y, ATT = 0, iterations = 1000,
  method = "linear")
}
\arguments{
\item{formula}{An object of class formula (or one that can be coerced to
that class): a symbolic description of the model to be fitted.}

\item{data}{An optional data frame, list or environment (or object coercible
by as.data.frame to a data frame) containing the variables in the model. If
not found in data, the variables are taken from environment(formula),
typically the environment from which CBPS is called.}

\item{na.action}{A function which indicates what should happen when the data
contain NAs. The default is set by the na.action setting of options, and is
na.fail if that is unset.}

\item{y}{An outcome variable.}

\item{ATT}{Option to calculate ATT}

\item{iterations}{An optional parameter for the maximum number of iterations
for the optimization. Default is 1000.}

\item{method}{Choose among "linear", "binomial", and "possion".}
}
\value{
\item{ATT}{Average treatment effect on the treated.}
\item{ATE}{Average treatment effect.} 
\item{s}{Standard Error.}
\item{fitted.values}{The fitted propensity score}
\item{coefficients1}{Coefficients for the treated propensity score}
\item{coefficients0}{Coefficients for the untreated propensity score}
\item{model}{The model frame}
}
\description{
hdCBPS high dimensional CBPS method to parses the formula object and passes the result to hdCBPS.fit, which calculates ATE using CBPS method in a high dimensional setting.
}
\author{
Sida Peng
}