Zelig: Everyone's Statistical Software

#' Probit Regression for Dichotomous Dependent Variables
#' @param formula a symbolic representation of the model to be
#'   estimated, in the form \code{y ~ x1 + x2}, where \code{y} is the
#'   dependent variable and \code{x1} and \code{x2} are the explanatory
#'   variables, and \code{y}, \code{x1}, and \code{x2} are contained in the
#'   same dataset. (You may include more than two explanatory variables,
#'   of course.) The \code{+} symbol means ``inclusion'' not
#'   ``addition.'' You may also include interaction terms and main
#'   effects in the form \code{x1*x2} without computing them in prior
#'   steps; \code{I(x1*x2)} to include only the interaction term and
#'   exclude the main effects; and quadratic terms in the form
#'   \code{I(x1^2)}.
#' @param model the name of a statistical model to estimate.
#'   For a list of other supported models and their documentation see:
#'   \url{http://docs.zeligproject.org/articles/}.
#' @param data the name of a data frame containing the variables
#'   referenced in the formula or a list of multiply imputed data frames
#'   each having the same variable names and row numbers (created by
#'   \code{Amelia} or \code{\link{to_zelig_mi}}).
#' @param ... additional arguments passed to \code{zelig},
#'   relevant for the model to be estimated.
#' @param by a factor variable contained in \code{data}. If supplied,
#'   \code{zelig} will subset
#'   the data frame based on the levels in the \code{by} variable, and
#'   estimate a model for each subset. This can save a considerable amount of
#'   effort. You may also use \code{by} to run models using MatchIt
#'   subclasses.
#' @param cite If is set to 'TRUE' (default), the model citation will be printed
#'   to the console.
#' @examples
#' data(turnout)
#' z.out <- zelig(vote ~ race + educate, model = "probit", data = turnout)
#' summary(z.out)
#' x.out <- setx(z.out)
#' s.out <- sim(z.out, x = x.out)
#' summary(s.out)
#' plot(s.out)
#'
#' @details
#' Additional parameters avaialable to this model include:
#' \itemize{
#'   \item \code{weights}: vector of weight values or a name of a variable in the dataset
#'   by which to weight the model. For more information see:
#'   \url{http://docs.zeligproject.org/articles/weights.html}.
#'   \item \code{bootstrap}: logical or numeric. If \code{FALSE} don't use bootstraps to
#'   robustly estimate uncertainty around model parameters due to sampling error.
#'   If an integer is supplied, the number of boostraps to run.
#'   For more information see:
#'   \url{http://docs.zeligproject.org/articles/bootstraps.html}.
#' }
#'
#' @seealso Vignette: \url{http://docs.zeligproject.org/articles/zelig_probit.html}
#' @import methods
#' @export Zelig-probit
#' @exportClass Zelig-probit
#'
#' @include model-zelig.R
#' @include model-glm.R
#' @include model-binchoice.R

zprobit <- setRefClass("Zelig-probit",
                       contains = "Zelig-binchoice")

zprobit$methods(
  initialize = function() {
    callSuper()
    .self$name <- "probit"
    .self$link <- "probit"
    .self$description = "Probit Regression for Dichotomous Dependent Variables"
    .self$packageauthors <- "R Core Team"
    .self$wrapper <- "probit"
  }
)

zprobit$methods(
  mcfun = function(x, b0=0, b1=1, ..., sim=TRUE){
    mu <- pnorm(b0 + b1 * x)
    if(sim){
        y <- rbinom(n=length(x), size=1, prob=mu)
        return(y)
    }else{
        return(mu)
    }
  }
)