get.confusion_matrix: Create confusion table and calculate the Balanced Error Rate
In mixOmics: Omics Data Integration Project

Description Usage Arguments Details Value Author(s) References See Also Examples

Create confusion table between a vector of true classes and a vector of predicted classes, calculate the Balanced Error rate

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get.confusion_matrix(truth, all.levels, predicted)

get.BER(confusion)

`truth`	A factor vector indicating the true classes of the samples (typically `Y` from the training set).
`all.levels`	Levels of the 'truth' factor. Optional parameter if there are some missing levels in `truth` compared to the fitted predicted model
`predicted`	Vector of predicted classes (typically the prediction from the test set). Can contain NA.
`confusion`	result from a `get.confusion_matrix` to calculate the Balanced Error Rate

BER is appropriate in case of an unbalanced number of samples per class as it calculates the average proportion of wrongly classified samples in each class, weighted by the number of samples in each class. BER is less biased towards majority classes during the performance assessment.

get.confusion_matrix returns a confusion matrix. get.BER returns the BER from a confusion matrix

Florian Rohart, Al J Abadi

mixOmics article:

Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752

predict.

data(liver.toxicity)
X <- liver.toxicity$gene
Y <- as.factor(liver.toxicity$treatment[, 4])

## if training is perfomed on 4/5th of the original data
samp <- sample(1:5, nrow(X), replace = TRUE)
test <- which(samp == 1)   # testing on the first fold
train <- setdiff(1:nrow(X), test)

plsda.train <- plsda(X[train, ], Y[train], ncomp = 2)
test.predict <- predict(plsda.train, X[test, ], dist = "max.dist")
Prediction <- test.predict$class$max.dist[, 2]

# the confusion table compares the real subtypes with the predicted subtypes for a 2 component model
confusion.mat = get.confusion_matrix(truth = Y[test],
predicted = Prediction)

get.BER(confusion.mat)