negative predictive value

npv.factor

R Documentation

Description

The npv()-function computes the negative predictive value, also known as the True Negative Predictive Value, between two vectors of predicted and observed factor() values. The weighted.npv() function computes the weighted negative predictive value.

Usage

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

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

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

npv(...)

weighted.npv(...)

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,

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

Where \(\#TN_k\) and \(\#FN_k\) are the number of true negatives and false 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 Negative Predictive Value

# 4.1) unweighted Negative Predictive Value
npv(
  actual    = actual,
  predicted = predicted
)

# 4.2) weighted Negative Predictive Value
weighted.npv(
  actual    = actual,
  predicted = predicted,
  w         = iris$Petal.Length/mean(iris$Petal.Length)
)

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