R/int_estimate_sensitivity.R

In statConfR: Models of Decision Confidence and Measures of Metacognition

estimate_sensitivity <- function(estimated_classifier){

  # Count number of predictions
  n_responses <- ncol(estimated_classifier)
  idx_predicting_first_row  <- 1:(n_responses/2)
  idx_predicting_second_row <- (n_responses/2 + 1):n_responses

  # Count number of correct predictions for first stimulus
  n_correctly_predict_first_row <- sum(estimated_classifier[1, idx_predicting_first_row])
  n_predict_first_row           <- sum(estimated_classifier[ , idx_predicting_first_row])

  # Count number of correct predictions for second stimulus
  n_correctly_predict_second_row <- sum(estimated_classifier[2, idx_predicting_second_row])
  n_predict_second_row           <- sum(estimated_classifier[ , idx_predicting_second_row])

  # Avoid infinite values by using Miller's (1996) 0.5-convention
  # (originally suggested by Murdock and Ogilvie, 1968).
  if (n_correctly_predict_first_row == n_predict_first_row)
    n_correctly_predict_first_row <- n_correctly_predict_first_row - 0.5
  if (n_correctly_predict_first_row == 0)
    n_correctly_predict_first_row <- 0.5
  if (n_correctly_predict_second_row == n_predict_second_row)
    n_correctly_predict_second_row <- n_correctly_predict_second_row - 0.5
  if (n_correctly_predict_second_row == 0)
    n_correctly_predict_second_row <- 0.5

  # Calculcate sensitivity
  hit_rate         <- n_correctly_predict_first_row / n_predict_first_row
  false_alarm_rate <- 1 - ( n_correctly_predict_second_row / n_predict_second_row )
  sensitivity      <- qnorm(hit_rate) - qnorm(false_alarm_rate)
  sensitivity
}