confusion matrix

cmatrix.factor

R Documentation

Description

The cmatrix()-function uses cross-classifying factors to build a confusion matrix of the counts at each combination of the factor levels. Each row of the matrix represents the actual factor levels, while each column represents the predicted factor levels.

Usage

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

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

cmatrix(...)

weighted.cmatrix(...)

Arguments

`actual`	A `<factor>`-vector of length \(n\), and \(k\) levels.
`predicted`	A `<factor>`-vector of length \(n\), and \(k\) levels.
`…`	Arguments passed into other methods.
`w`	A `<numeric>`-vector of length \(n\) (default: NULL) If passed it will return a weighted confusion matrix.

Value

A named \(k\) x \(k\) <matrix> of class

Dimensions

There is no robust defensive measure against misspecififying the confusion matrix. If the arguments are correctly specified, the resulting confusion matrix is on the form:

	A (Predicted)	B (Predicted)
A (Actual)	Value	Value
B (Actual)	Value	Value

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) summarise performance
# in a confusion matrix

# 4.1) unweighted matrix
confusion_matrix <- cmatrix(
  actual    = actual,
  predicted = predicted
)

# 4.1.1) summarise matrix
summary(
  confusion_matrix
)

# 4.1.2) plot confusion
# matrix
plot(
  confusion_matrix
)

# 4.2) weighted matrix
confusion_matrix <- weighted.cmatrix(
  actual    = actual,
  predicted = predicted,
  w         = iris$Petal.Length/mean(iris$Petal.Length)
)

# 4.2.1) summarise matrix
summary(
  confusion_matrix
)

# 4.2.1) plot confusion
# matrix
plot(
  confusion_matrix
)