R/sensitivities.R

In frankchien/bmi585flc: BMI 585 Final- Biostatistical Functions.

#' @title
#' Sensitivity
#'
#' @description
#' Computes sensitivity given set of predictions and set of ground truths
#'
#' @details
#' Takes in vectors representing a prediction and a ground truth.
#' Vectors may be either logical using T/F or numeric using 1 and 0s.
#' Vectors must be of the same length
#'
#' @param pred vector of 1s and 0s or True and False representing predictions
#' @param truth vector of 1s and 0s or True and False representing gold standards or ground truths
#'
#' @return  sensitivity given as true pos/(true pos + false neg)
#'
#' @examples
#' pred1 <- c(1,1,0,1,1,0,0)
#' truth1 < c(1,0,0,1,1,0,1)
#' sensitivity(pred1,truth1)
#'
#' pred2 <- c(T,T,F,T,T,F,F)
#' truth2 <- c(T,F,F,T,T,F,T)
#' sensitivity(pred2, truth2)

sensitivity <- function(pred, truth){ #takes either logical vectors or 1 and 0s
  pred <- as.logical(pred)
  truth <- as.logical(truth)
  num_true_pos <- length(which(pred&truth))
  num_true_neg <- length(which(!pred&!truth))
  num_false_pos <- length(which(pred&!truth))
  num_false_neg <- length(which(!pred&truth))
  counts <- c(num_true_pos,num_true_neg,num_false_pos,num_false_neg)
  #sensitivity = true pos / (true pos + false neg)
  counts[1] / (counts[1] + counts[4]) #see comments for get counts for indices
}