Measures by threshold

Computes measures according to a moving threshold.

Usage

ebc_tidy_by_threshold(
  detection_values,
  true,
  all,
  m = length(all),
  measures = c("TPR", "FPR", "FDR", "ACC", "F1"),
  direction = c("<", ">", "<=", ">=")
)

Arguments

detection_values

Values corresponding to elements that are detected. Must be named.

true

Vector of element that are supposed to be detected.

all

Vector of all elements.

m

Total number of elements.

measures

Desired measures of performance.

direction

With < (default), detected elements are those which are strictly less than the threshold. Could be change to ">", <= or >=.

Value

A dataframe with one column called threshold and other corresponding to those specified in measures.

Details

See ebc_allmeasures for the available measures and their descriptions.

Examples

set.seed(42)
X1 <- rnorm(50)
X2 <- rnorm(50)
X3 <- rnorm(50)
predictors <- paste0("X", 1:3)
df_lm <- data.frame(X1 = X1, X2 = X2, X3 = X3,
                    X4 = X1 + X2 + X3 + rnorm(50, sd = 0.5),
                    X5 = X1 + 3 * X3 + rnorm(50, sd = 0.5),
                    X6 = X2 - 2 * X3 + rnorm(50, sd = 0.5),
                    X7 = X1 - X2 + rnorm(50, sd = 2),
                    Y  = X1 - X2 + 3 * X3 + rnorm(50))
model <- lm(Y ~ ., data = df_lm)
pvalues <- summary(model)$coefficients[-1, 4]
ebc_tidy_by_threshold(pvalues, predictors, m = 7)
#>     threshold       TPR  FPR       FDR       ACC        F1
#> 1 0.003469737 0.0000000 0.00       NaN 0.5714286 0.0000000
#> 2 0.004366456 0.3333333 0.00 0.0000000 0.7142857 0.5000000
#> 3 0.173677616 0.6666667 0.00 0.0000000 0.8571429 0.8000000
#> 4 0.449664443 1.0000000 0.00 0.0000000 1.0000000 1.0000000
#> 5 0.491828466 1.0000000 0.25 0.2500000 0.8571429 0.8571429
#> 6 0.581608670 1.0000000 0.50 0.4000000 0.7142857 0.7500000
#> 7 0.887948400 1.0000000 0.75 0.5000000 0.5714286 0.6666667
#> 8         Inf 1.0000000 1.00 0.5714286 0.4285714 0.6000000