Calculate all measures for sparse ROC

Usage

getThresholdSummary(
  prediction,
  predictionType = "binary",
  typeColumn = "evaluation"
)

Arguments

prediction: A prediction object
predictionType: The type of prediction (binary or survival)
typeColumn: A column that is used to stratify the results

Value

A data.frame with TP, FP, TN, FN, TPR, FPR, accuracy, PPF, FOR and Fmeasure

Details

Calculates the TP, FP, TN, FN, TPR, FPR, accuracy, PPF, FOR and Fmeasure from a prediction object

Examples

prediction <- data.frame(rowId = 1:100, 
                         outcomeCount = stats::rbinom(1:100, 1, prob=0.5),
                         value = runif(100), 
                         evaluation = rep("Train", 100))
summary <- getThresholdSummary(prediction)
str(summary)
#> 'data.frame':	100 obs. of  24 variables:
#>  $ evaluation             : chr  "Train" "Train" "Train" "Train" ...
#>  $ predictionThreshold    : num  0.995 0.988 0.986 0.975 0.969 ...
#>  $ preferenceThreshold    : num  0.994 0.986 0.983 0.969 0.962 ...
#>  $ positiveCount          : num  1 2 3 4 5 6 7 8 9 10 ...
#>  $ negativeCount          : num  99 98 97 96 95 94 93 92 91 90 ...
#>  $ trueCount              : num  55 55 55 55 55 55 55 55 55 55 ...
#>  $ falseCount             : num  45 45 45 45 45 45 45 45 45 45 ...
#>  $ truePositiveCount      : num  0 1 2 3 3 3 4 4 5 6 ...
#>  $ trueNegativeCount      : num  44 44 44 44 43 42 42 41 41 41 ...
#>  $ falsePositiveCount     : num  1 1 1 1 2 3 3 4 4 4 ...
#>  $ falseNegativeCount     : num  55 54 53 52 52 52 51 51 50 49 ...
#>  $ f1Score                : num  NaN 0.0351 0.069 0.1017 0.1 ...
#>  $ accuracy               : num  0.44 0.45 0.46 0.47 0.46 0.45 0.46 0.45 0.46 0.47 ...
#>  $ sensitivity            : num  0 0.0182 0.0364 0.0545 0.0545 ...
#>  $ falseNegativeRate      : num  1 0.982 0.964 0.945 0.945 ...
#>  $ falsePositiveRate      : num  0.0222 0.0222 0.0222 0.0222 0.0444 ...
#>  $ specificity            : num  0.978 0.978 0.978 0.978 0.956 ...
#>  $ positivePredictiveValue: num  0 0.5 0.667 0.75 0.6 ...
#>  $ falseDiscoveryRate     : num  1 0.5 0.333 0.25 0.4 ...
#>  $ negativePredictiveValue: num  0.444 0.449 0.454 0.458 0.453 ...
#>  $ falseOmissionRate      : num  0.556 0.551 0.546 0.542 0.547 ...
#>  $ positiveLikelihoodRatio: num  0 0.818 1.636 2.455 1.227 ...
#>  $ negativeLikelihoodRatio: num  1.023 1.004 0.986 0.967 0.989 ...
#>  $ diagnosticOddsRatio    : num  0 0.815 1.66 2.538 1.24 ...