fourfoldplot: A prettier confusion matrix in base R
1 min readJan 17, 2017
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 examplelvs <- 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.