Diagnostic Odds Ratio

dor.factor

R Documentation

Description

A generic function for the diagnostic odds ratio in classification tasks. Use weighted.dor() weighted diagnostic odds ratio.

Usage

## S3 method for class 'factor'
dor(actual, predicted, ...)

## S3 method for class 'factor'
weighted.dor(actual, predicted, w, ...)

## S3 method for class 'cmatrix'
dor(x, ...)

## Generic S3 method
dor(...)

## Generic S3 method
weighted.dor(
 ...,
 w
)

Arguments

`actual`	A vector of - of length \(n\), and \(k\) levels.
`predicted`	A vector of -vector of length \(n\), and \(k\) levels.
`…`	micro = NULL, na.rm = TRUE Arguments passed into other methods
`w`	A `<numeric>`-vector of length \(n\). NULL by default.
`x`	A confusion matrix created `cmatrix()`.

Value

A <numeric>-vector of length 1

Definition

Let \(\hat{\alpha} \in [0, \infty]\) be the effectiveness of the classifier. The diagnostic odds ratio of the classifier is calculated as,

\[ \hat{\alpha} = \frac{\text{\#TP} \text{\#TN}}{\text{\#FP} \text{\#FN}} \]

Where:

\(\text{\#TP}\) is the number of true positives
\(\text{\#TN}\) is the number of true negatives
\(\text{\#FP}\) is the number of false positives
\(\text{\#FN}\) is the number of false negatives

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 model performance
# with Diagnostic Odds Ratio
cat("Diagnostic Odds Ratio", sep = "\n")
dor(
  actual    = actual, 
  predicted = predicted
)

cat("Diagnostic Odds Ratio (weighted)", sep = "\n")
weighted.dor(
  actual    = actual,
  predicted = predicted,
  w         = iris$Petal.Length/mean(iris$Petal.Length)
)