R/multi_transmat.R

In soodoku/guess: Adjust Estimates of Learning for Guessing

#' multi_transmat: transition matrix of all the items
#'
#' @title Creates a transition matrix for each item.
#' @description Needs an 'interleaved' dataframe (see interleave function). Pre-test item should be followed by corresponding post-item item etc. 
#' Don't knows must be coded as NA. Function handles items without don't know responses.
#' The function is used internally. It calls transmat.
#' @param pre_test Required. data.frame carrying responses to pre-test questions.
#' @param pst_test Required. data.frame carrying responses to post-test questions.
#' @param subgroup a Boolean vector identifying the subset. Default is NULL.
#' @param force9   Optional. There are cases where DK data doesn't have DK. But we need the entire matrix. By default it is FALSE.
#' @param agg      Optional. Boolean. Whether or not to add a row of aggregate transitions at the end of the matrix. Default is FALSE.
#' @return matrix with rows = total number of items + 1 (last row contains aggregate distribution across items)
#' number of columns = 4 when no don't know, and 9 when there is a don't know option
#' @export
#' @examples
#' pre_test <- data.frame(pre_item1 = c(1,0,0,1,0), pre_item2 = c(1,NA,0,1,0)) 
#' pst_test <- data.frame(pst_item1 = pre_test[,1] + c(0,1,1,0,0), 
#'              pst_item2 = pre_test[,2] + c(0,1,0,0,1))
#' multi_transmat(pre_test, pst_test)

multi_transmat <- function (pre_test = NULL, pst_test = NULL,
                            subgroup = NULL, force9 = FALSE, agg = FALSE) {

  # Checks
  # pre_test data frame is missing
  if (!is.data.frame(pre_test)) stop("Specify pre_test data.frame.")

  # post_test data frame is missing
  if (!is.data.frame(pst_test)) stop("Specify pst_test data.frame.")

  # If different no. of items
  if (length(pre_test) != length(pst_test)) {
    stop("Lengths of pre_test and pst_test must be the same.")
  }

  # Subset
  if (!is.null(subgroup)) {

    pre_test <- subset(pre_test, subgroup)
    pst_test <- subset(pst_test, subgroup)
  }

  # No. of items
  n_items <- length(pre_test)

  # Initialize results
  res <- list()

  # Get transition matrix for each item pair
  for (i in 1:n_items) {

    # cat("\n Item", i, "\n")
    res[[i]] <- transmat(pre_test[, i], pst_test[, i], force9 = force9)
  }

  # Prepping results
  row_names <- paste0("item", 1:n_items)
  col_names <- names(res[[1]])

  res       <- matrix(unlist(res),
                      nrow = n_items,
                      byrow = TRUE,
                      dimnames = list(row_names, col_names))

  if (agg == TRUE) {

    res       <- rbind(res, colSums(res, na.rm = T))
    rownames(res)[nrow(res)] <- "agg"
  }

  invisible(res)
}