specificity

specificity.factor

R Documentation

Description

The specificity()-function computes the specificity, also known as the True Negative Rate (TNR) or selectivity, between two vectors of predicted and observed factor() values. The weighted.specificity() function computes the weighted specificity.

Usage

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

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

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

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

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

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

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

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

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

specificity(...)

tnr(...)

selectivity(...)

weighted.specificity(...)

weighted.tnr(...)

weighted.selectivity(...)

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

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 Specificity

# 4.1) unweighted Specificity
specificity(
  actual    = actual,
  predicted = predicted
)

# 4.2) weighted Specificity
weighted.specificity(
  actual    = actual,
  predicted = predicted,
  w         = iris$Petal.Length/mean(iris$Petal.Length)
)

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