R/specificity.r

In PNNL-Comp-Mass-Spec/glmnetGLR: glmnetGLR

#' Computes classification specificity from the confusion matrix summary based on
#' a set of predicted and truth classes for a signature.
#'
#' For each class, we calculate the classification specificity in order to
#' summarize its performance for the signature. We compute one of two aggregate
#' scores, to summarize the overall performance of the signature.
#'
#' To estimate specificity_j for the jth class, we compute
#'
#' (TN_j) / (TN_j + FP_j),
#'
#' where TN_j and FP_j are the true negatives and false positives,
#' respectively. More specifically, TN_j is the number of observations
#' that we correctly classified into other classes than the jth class, and FP_j
#' is the number of observations that we have incorrectly classified into class
#' j.
#'
#' The two aggregate score options are the macro- and micro-aggregate (average)
#' scores. The macro-aggregate score is the arithmetic mean of the binary scores
#' for each class. The micro-aggregate score is a weighted average of each class'
#' binary score, where the weights are determined by the sample sizes for each
#' class. By default, we use the micro-aggregate score because it is more robust,
#' but the macro-aggregate score might be more intuitive to some users.
#'
#' Notice that the specificity is equal to the TNR.
#'
#' The specificity measure ranges from 0 to 1 with 1 being the optimal value.
#'
#' @export
#' 
#' @rdname specificity
#' 
#' @param confusionSummary list containing the confusion summary for a set of
#' classifications
#' @param aggregate string that indicates the type of aggregation; by default,
#' micro. See details.
#' @return list with the accuracy measure for each class as well as the macro-
#' and micro-averages (aggregate measures across all classes).
#' 
#' @examples
#' 
#' data(prediction_values)
#' 
#' confmat <- confusion(prediction_values[,"Curated_Quality"], prediction_values[,"PredictClass"])
#' 
#' specificity(confmat)
 
specificity <- function(confusionSummary, aggregate = c('micro', 'macro')) {
  aggregate <- match.arg(aggregate)
  
  byClass <- sapply(confusionSummary$classSummary, function(clSummary) {
    with(clSummary,
         trueNeg / (trueNeg + falsePos)
    )
  })
  names(byClass) <- names(confusionSummary$classSummary)

  if (aggregate == 'micro') {
    numerator <- sum(sapply(confusionSummary$classSummary, function(clSummary) {
      clSummary$trueNeg
    }))
    denom <- sum(sapply(confusionSummary$classSummary, function(clSummary) {
      with(clSummary, trueNeg + falsePos)
    }))
    aggregate <- numerator / denom
  } else {
    aggregate <- mean(byClass)
  }

  list(byClass = byClass, aggregate = aggregate)
}