R/accuracy.r

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

#' Computes classification accuracy from the confusion matrix summary based on a
#' set of predicted and truth classes for a signature.
#'
#' For each class, we calculate the classification accuracy in order to summarize
#' its performance for the signature. We compute one of two aggregate scores,
#' to summarize the overall performance of the signature.
#' 
#' We define the accuracy as the proportion of correct classifications.
#'
#' 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.
#'
#' Note that the macro- and micro-aggregate scores are the same for classification
#' accuracy.
#'
#' The accuracy measure ranges from 0 to 1 with 1 being the optimal value.
#'
#' @export
#' 
#' @rdname accuracy
#' 
#' @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)
#' 
#' # Create the confusion matrix
#' confmat <- confusion(prediction_values[,"Curated_Quality"], prediction_values[,"PredictClass"])
#' 
#' accuracy(confmat)
#' 

accuracy <- function(confusionSummary, aggregate = c('micro', 'macro')) {
  aggregate <- match.arg(aggregate)
  
  byClass <- sapply(confusionSummary$classSummary, function(clSummary) {
    with(clSummary,
         (truePos + trueNeg) / (truePos + trueNeg + falsePos + falseNeg)
    )
  })
  names(byClass) <- names(confusionSummary$classSummary)

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

  list(byClass = byClass, aggregate = aggregate)
}