R/observed.R

In blatent: Bayesian Latent Variable Models

#' @title Declares observed variables in a blatent model
#'
#' @description Used in \code{\link{blatentSyntax}} to declare the distribution and link function for observed variables
#'   as an unevaluated function call. Sets specifications used in estimation.
#'
#' @param distribution Specifies the distribution of the observed variable(s) to which the function points. Defaults to \code{"bernoulli"}.
#'  Distributions currently available are:
#' \itemize{
#'   \item \code{"bernoulli"}: Specifies each variable follows a Bernoulli distribution.
#' }
#'
#' @param link Specifies the link function used for any observed variable model where the observed variable is predicted.
#'   Defaults to \code{"probit"}. Link functions currently available are:
#' \itemize{
#'   \item \code{"probit"}: Uses a probit link function. Available for variables where \code{distribution = "bernoulli"} only.
#' }
#'
observed = function(distribution = 'bernoulli', link = 'probit'){


  if (distribution == "bernoulli"){
    generationFunction = generateBernoulliWrap
    #note: choice will depend on estimator. Estimator package will have to filter for correct link function
    if (link == "probit"){
      linkFunction = stats::qnorm
      inverseLinkFunction = stats::pnorm
    } else if (link == "logit"){
      linkFunction = function(p) {return(log(p/(1-p)))}
      inverseLinkFunction = function(q) {return( (exp(q)/(1+exp(q))) )}
    } else {
      stop(paste0("link function ", link, " for Bernoulli distributed variables not supported"))
    }

  } else {
    # for later
    stop(paste0("distribution ", distribution, " not supported"))
  }

  return(
    list(
      distribution = distribution,
      generationFunction = generationFunction,
      inverseLinkFunction = inverseLinkFunction,
      isLatent = FALSE,
      isObserved = TRUE,
      linkFunction = linkFunction,
      link = link,
      structure = NA,
      type = "bernoulli",
      unit = NA
    )
  )

}