confMatrix {MKclass}

Compute Confusion Matrix

Description

The function computes the confusion matrix of a binary classification.

Usage

confMatrix(pred, pred.group, truth, namePos, cutoff = 0.5, relative = TRUE)

Arguments

pred

numeric values that shall be used for classification; e.g. probabilities to belong to the positive group.

pred.group

vector or factor including the predicted group. If missing, pred.group is computed from pred, where pred >= cutoff is classified as positive.

truth

true grouping vector or factor.

namePos

value representing the positive group.

cutoff

cutoff value used for classification.

relative

logical: absolute and relative values.

Details

The function computes the confusion matrix of a binary classification consisting of the number of true positive (TP), false negative (FN), false positive (FP) and true negative (TN) predictions.

In addition, their relative counterparts true positive rate (TPR), false negative rate (FNR), false positive rate (FPR) and true negative rate (TNR) can be computed.

Value

matrix or list of matrices with respective numbers of true and false predictions.

Author(s)

Matthias Kohl [e-mail]

References

Wikipedia contributors. (2019, July 18). Confusion matrix. In Wikipedia, The Free Encyclopedia. Retrieved 06:00, August 21, 2019, from https://en.wikipedia.org/w/index.php?title=Confusion_matrix&oldid=906886050

Examples

## example from dataset infert
fit <- glm(case ~ spontaneous+induced, data = infert, family = binomial())
pred <- predict(fit, type = "response")

## with group numbers
confMatrix(pred, truth = infert$case, namePos = 1)

## with group names
my.case <- factor(infert$case, labels = c("control", "case"))
confMatrix(pred, truth = my.case, namePos = "case")

## on the scale of the linear predictors
pred2 <- predict(fit)
confMatrix(pred2, truth = infert$case, namePos = 1, cutoff = 0)

## only absolute numbers
confMatrix(pred, truth = infert$case, namePos = 1, relative = FALSE)