# fourfoldplot: A prettier confusion matrix in base R

Working with R, it’s high likely you end with a 2 by 2 table regarding to dichotomous variables in your datasets no matter the specific project you’re involved in.

I like the ConfusionMatrix function from caret package, that calculates a cross-tabulation of observed and predicted classes. Here an example from caret vignette.

library(caret)

## 2 class example

lvs <- c(“normal”, “abnormal”)

truth <- factor(rep(lvs, times = c(86, 258)),

levels = rev(lvs))

pred <- factor(

c(rep(lvs, times = c(54, 32)), rep(lvs, times = c(27, 231))), levels = rev(lvs))

xtab <- table(pred, truth)

cm <- confusionMatrix(pred, truth)

cm$table

The confusion matrix renders as follows:

Reference

Prediction abnormal normal

abnormal 231 32

normal 27 54

Taking this confusion table, simple and informative, but just figures. There’s a useful addition to your analysis using fourfoldplot from base R.

fourfoldplot(cm$table)

Pretty neat and a cool addition to your reproducible research to be shared.