# Estimate a One-Class SVM model
trainRows <- c(1:30, 51:80, 101:130)
testRows = !(1:150 %in% trainRows)
trainIris <- iris[trainRows,]
testIris <- iris[testRows,]

trainIris$id <- seq.int(nrow(trainIris))
svmModel <- rxOneClassSvm(
  formula = ~Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
  data = trainIris)

# Add additional non-iris data to the test data set
testIris$isIris <- 1
notIris <- data.frame(
  Sepal.Length = c(2.5, 2.6),
  Sepal.Width = c(.75, .9),
  Petal.Length = c(2.5, 2.5),
  Petal.Width = c(.8, .7),
  Species = c("not iris", "not iris"),
  isIris = 0)
testIris <- rbind(testIris, notIris)

testIris$id <- seq.int(nrow(testIris))
scoreDF <- rxPredict(svmModel,
                     data = testIris, extraVarsToWrite = c("isIris", "id"))

# Look at the last few observations
tail(scoreDF)
# Look at average scores conditioned by 'isIris'
rxCube(Score ~ F(isIris), data = scoreDF)

rxLinePlot(Score~id, type = c("p"), data = scoreDF,
           title = "Scores from rxOneClassSvm",
           symbolColor = ifelse(scoreDF$isIris == 0, "red", "blue"))