R/fit_nodk.R

In soodoku/guess: Adjust Estimates of Learning for Guessing

#' fit_nodk
#'
#' @title Goodness of fit statistics for data without don't know
#' 
#' @description For data without Don't Know, chi-square goodness of fit between true and model based multivariate distribution
#' 
#' @param pre_test data.frame carrying pre_test items
#' @param pst_test data.frame carrying pst_test items 
#' @param g     estimates of \eqn{\gamma} produced from \code{\link{lca_cor}}
#' @param est.param estimated parameters produced from \code{\link{lca_cor}}
#' 
#' @return matrix with two rows: top row carrying chi-square value, and bottom row probability of observing that value
#' 
#' @export
#' 
#' @examples
#' \dontrun{fit_nodk(pre_test, pst_test, g, est.param)}

fit_nodk <- function(pre_test, pst_test, g, est.param) {

  data    <- multi_transmat(pre_test, pst_test)
  data    <- data[(1:nrow(data) - 1), ] # remove the agg.
  expec  <- matrix(ncol = nrow(data), nrow = 4)
  fit    <- matrix(ncol = nrow(data), nrow = 2)
  colnames(fit) <- rownames(data)
  rownames(fit) <- c("chi-square", "p-value")

  for (i in 1:nrow(data)) {

    gi      <- g[[i]]
    expec[1, i]  <- (1 - gi) * (1 - gi) * est.param[1, i] * sum(data[i, ])
    expec[2, i]  <- ( (1 - gi) * gi * est.param[1, i] +
                    (1 - gi) * est.param[2, i]) * sum(data[i, ])
    expec[3, i]  <- (1 - gi) * est.param[3, i] * est.param[1, i] *
                    sum(data[i, ])
    expec[4, i]  <- (gi * gi * est.param[1, i] + gi * est.param[2, i] +
                     est.param[3, i]) * sum(data[i, ])
    test     <- suppressWarnings(chisq.test(expec[, i],
                                            p = data[i, ] / sum(data[i, ])))
    fit[1:2, i]  <- unlist(test[c(1, 3)])
  }

  fit
}