Out-of-Sample Application

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.

# S3 method for class '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,
  ...
)

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.

Value

An object of class "outForest".

See also

outForest(), outliers(), Data()

Examples

(out <- outForest(iris, allow_predictions = TRUE))
#> 
#> Outlier identification by random forests
#> 
#>   Variables to check:		Sepal.Length, Sepal.Width, Petal.Length, Petal.Width
#>   Variables used to check:	Sepal.Length, Sepal.Width, Petal.Length, Petal.Width, Species
#> 
#>   Checking: Sepal.Length  Sepal.Width  Petal.Length  Petal.Width  
#> I am an object of class(es) outForest and list 
#> 
#> The following number of outliers have been identified:
#> 
#>              Number of outliers
#> Sepal.Length                  1
#> Sepal.Width                   1
#> Petal.Length                  2
#> Petal.Width                   2
iris1 <- iris[1, ]
iris1$Sepal.Length <- -1
pred <- predict(out, newdata = iris1)
outliers(pred)
#>   row          col observed predicted      rmse     score threshold replacement
#> 1   1 Sepal.Length       -1  5.004353 0.3691353 -16.26599         3           5
Data(pred)
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1            5         3.5          1.4         0.2  setosa
plot(pred)

plot(pred, what = "scores")