false omission rate

fer.factor

R Documentation

Description

The fer()-function computes the false omission rate (FOR), the proportion of false negatives among the predicted negatives, between two vectors of predicted and observed factor() values. The weighted.fer() function computes the weighted false omission rate.

Usage

## S3 method for class 'factor'
fer(actual, predicted, micro = NULL, na.rm = TRUE, ...)

## S3 method for class 'factor'
weighted.fer(actual, predicted, w, micro = NULL, na.rm = TRUE, ...)

## S3 method for class 'cmatrix'
fer(x, micro = NULL, na.rm = TRUE, ...)

fer(...)

weighted.fer(...)

Arguments

actual	A vector of <factor>- of length \(n\), and \(k\) levels.
predicted	A vector of <factor>-vector of length \(n\), and \(k\) levels.
micro	A <logical>-value of length \(1\) (default: NULL). If TRUE it returns the micro average across all \(k\) classes, if FALSE it returns the macro average.
na.rm	A <logical> value of length \(1\) (default: TRUE). If TRUE, NA values are removed from the computation. This argument is only relevant when micro != NULL. When na.rm = TRUE, the computation corresponds to sum(c(1, 2, NA), na.rm = TRUE) / length(na.omit(c(1, 2, NA))). When na.rm = FALSE, the computation corresponds to sum(c(1, 2, NA), na.rm = TRUE) / length(c(1, 2, NA)).
…	Arguments passed into other methods
w	A <numeric>-vector of length \(n\). NULL by default.
x	A confusion matrix created cmatrix().

Value

If micro is NULL (the default), a named <numeric>-vector of length k

If micro is TRUE or FALSE, a <numeric>-vector of length 1

Calculation

The metric is calculated for each class \(k\) as follows,

\[ \frac{\#FN_k}{\#FN_k + \#TN_k} \]

Where \(\#FN_k\) and \(\#TN_k\) are the number of false negatives and true negatives, respectively, for each class \(k\).

Examples

# 1) recode Iris
# to binary classification
# problem
iris$species_num <- as.numeric(
  iris$Species == "virginica"
)

# 2) fit the logistic
# regression
model <- glm(
  formula = species_num ~ Sepal.Length + Sepal.Width,
  data    = iris,
  family  = binomial(
    link = "logit"
  )
)

# 3) generate predicted
# classes
predicted <- factor(
  as.numeric(
    predict(model, type = "response") >` 0.5
  ),
  levels = c(1,0),
  labels = c("Virginica", "Others")
)

# 3.1) generate actual
# classes
actual <- factor(
  x = iris$species_num,
  levels = c(1,0),
  labels = c("Virginica", "Others")
)

# 4) evaluate class-wise performance
# using False Omission Rate

# 4.1) unweighted False Omission Rate
fer(
  actual    = actual,
  predicted = predicted
)

# 4.2) weighted False Omission Rate
weighted.fer(
  actual    = actual,
  predicted = predicted,
  w         = iris$Petal.Length/mean(iris$Petal.Length)
)

# 5) evaluate overall performance
# using micro-averaged False Omission Rate
cat(
  "Micro-averaged False Omission Rate", fer(
    actual    = actual,
    predicted = predicted,
    micro     = TRUE
  ),
  "Micro-averaged False Omission Rate (weighted)", weighted.fer(
    actual    = actual,
    predicted = predicted,
    w         = iris$Petal.Length/mean(iris$Petal.Length),
    micro     = TRUE
  ),
  sep = "\n"
)