outForest (version 1.0.1)

predict.outForest: Out-of-Sample Application

Description

Identifies outliers in new data based on previously fitted "outForest" object. The result of predict() is again an object of class "outForest". All its methods can be applied to it.

Usage

# S3 method for outForest
predict(
  object,
  newdata,
  replace = c("pmm", "predictions", "NA", "no"),
  pmm.k = 3L,
  threshold = object$threshold,
  max_n_outliers = Inf,
  max_prop_outliers = 1,
  seed = NULL,
  ...
)

Value

An object of class "outForest".

Arguments

object

An object of class "outForest".

newdata

A new data.frame to be assessed for numeric outliers.

replace

Should outliers be replaced via predictive mean matching "pmm" (default), by "predictions", or by NA ("NA"). Use "no" to keep outliers as they are.

pmm.k

For replace = "pmm", from how many nearest OOB prediction neighbours (from the original non-outliers) to sample?

threshold

Threshold above which an outlier score is considered an outlier. The default is 3.

max_n_outliers

Maximal number of outliers to identify. Will be used in combination with threshold and max_prop_outliers.

max_prop_outliers

Maximal relative count of outliers. Will be used in combination with threshold and max_n_outliers.

seed

Integer random seed.

...

Further arguments passed from other methods.

See Also

outForest(), outliers(), Data()

Examples

(out <- outForest(iris, allow_predictions = TRUE))
iris1 <- iris[1, ]
iris1$Sepal.Length <- -1
pred <- predict(out, newdata = iris1)
outliers(pred)
Data(pred)
plot(pred)
plot(pred, what = "scores")