R/flexCItest.R

In micd: Multiple Imputation in Causal Graph Discovery

#' Wrapper for gaussCItest, disCItest and mixCItest
#'
#' A plug-in conditional independence test for [pcalg::skeleton()], [pcalg::pc()] or
#' [pcalg::fci()] when multiply imputed data sets are available. [flexCItest()] detects whether
#' variables are continuous, discrete or mixed, and automatically switches between [gaussCItest()] (continuous only),
#' [disCItest()] (discrete only) and [mixCItest()] (mixed variables).
#'
#' @param x,y,S (integer) position of variable X, Y and set of variables S,
#' respectively, in the dataset. It is tested whether X and Y are conditionally
#' independent given the subset S of the remaining variables.
#' @param suffStat a list generated using `getSuff()` with \code{test="flexMItest"}. See below for details.
#'
#' @details \code{suffStat} needs to be a list with four elements named \code{datlist}, \code{corlist},
#' \code{conpos} and \code{dispos}. \code{datlist} is the list of imputed datasets. \code{corlist}
#' is a list with M+1 elements, where M is the number of imputed datasets. For i=1,...,M, the
#' the i-th element of \code{corlist} is the correlation matrix of the continuous variables in the i-th imputed dataset;
#' the (M+1)-the element is the number of rows in each imputed dataset.
#' \code{conpos} is a vector containing the integer positions of the continuous variables in the original dataset.
#' \code{dispos} is a vector containing the integer positions of the discrete variables in the original dataset.
#'
#'
#' @return A p-value.
#'
#' @seealso [gaussCItest()], [disCItest()] and [mixCItest()].
#'
#' @examples
#' # load data (numeric and factor variables)
#' dat <- toenail2[1:400, ]
#'
#' # obtain correct input 'suffStat' for 'flexCItest'
#' suff <- getSuff(dat, test="flexCItest")
#' 
#' flexCItest(2,3,NULL, suffStat = suff)
#'
#' @export



flexCItest <- function(x, y, S = NULL, suffStat) {

  if (all(c(x,y,S) %in% suffStat$conpos)) {
    x2 <- as.numeric(which(suffStat$conpos %in% x))
    y2 <- as.numeric(which(suffStat$conpos %in% y))

    if (is.null(S)){
      S2 <- S
    } else {
      S2 <- which(suffStat$conpos %in% S)
    }
    
    pval <- pcalg::gaussCItest(x = x2, y = y2, S = S2, suffStat = suffStat$corlist)

  } else if (all(c(x,y,S) %in% suffStat$dispos) ) {
    x2 <- as.numeric(which(suffStat$dispos %in% x))
    y2 <- as.numeric(which(suffStat$dispos %in% y))

    if (is.null(S)){
      S2 <- S
    } else {
      S2 <- which(suffStat$dispos %in% S)
    }
    pval <- pcalg::disCItest(x = x2, y = y2, S = S2, suffStat = suffStat$datlist)

  } else {
    pval <- mixCItest(x = x, y = y, S = S, suffStat = suffStat$dat)
  }

  return(pval)
}