Extracts Data

Description

Extracts data with optionally replaced outliers from object of class "outForest".

Usage

Data(object, ...)

## Default S3 method:
Data(object, ...)

## S3 method for class 'outForest'
Data(object, ...)

Arguments

Value

A data.frame.

Methods (by class)

• Data(default): Default method not implemented yet.

• Data(outForest): Extract data from "outForest" object.

Examples

x <- outForest(iris)
head(Data(x))

Adds Outliers

Description

Takes a vector, matrix or data frame and replaces some numeric values by outliers.

Usage

generateOutliers(x, p = 0.05, sd_factor = 5, seed = NULL)

Arguments

Value

x with outliers.

See Also

outForest()

Examples

generateOutliers(1:10, seed = 334, p = 0.3)
generateOutliers(cbind(1:10, 10:1), p = 0.2)
head(generateOutliers(iris))
head(generateOutliers(iris, p = 0.2))
head(generateOutliers(iris, p = c(0, 0, 0.5, 0.5, 0.5)))
head(generateOutliers(iris, p = c(Sepal.Length = 0.2)))

Type Check

Description

Checks if an object inherits class "outForest".

Usage

is.outForest(x)

Arguments

Value

A logical vector of length one.

Examples

a <- outForest(iris)
is.outForest(a)
is.outForest("a")

Multivariate Outlier Detection and Replacement

Description

This function provides a random forest based implementation of the method described in Chapter 7.1.2 ("Regression Model Based Anomaly detection") of Chandola et al. Each numeric variable to be checked for outliers is regressed onto all other variables using a random forest. If the scaled absolute difference between observed value and out-of-bag prediction is larger than some predefined threshold (default is 3), then a value is considered an outlier, see Details below. After identification of outliers, they can be replaced, e.g., by predictive mean matching from the non-outliers.

Usage

outForest(
  data,
  formula = . ~ .,
  replace = c("pmm", "predictions", "NA", "no"),
  pmm.k = 3L,
  threshold = 3,
  max_n_outliers = Inf,
  max_prop_outliers = 1,
  min.node.size = 40L,
  allow_predictions = FALSE,
  impute_multivariate = TRUE,
  impute_multivariate_control = list(pmm.k = 3L, num.trees = 50L, maxiter = 3L),
  seed = NULL,
  verbose = 1,
  ...
)

Arguments

Details

The method can be viewed as a multivariate extension of a basic univariate outlier detection method where a value is considered an outlier if it is more than, e.g., three times the standard deviation away from its mean. In the multivariate case, instead of comparing a value with the overall mean, rather the difference to the conditional mean is considered. outForest() estimates this conditional mean by a random forest. If the method is trained on a reference data with option allow_predictions = TRUE, it can even be applied to new data.

The outlier score of the ith value x_{ij} of the jth variable is defined as s_{ij} = (x_{ij} - p_{ij}) / \textrm{rmse}_j, where p_{ij} is the corresponding out-of-bag prediction of the jth random forest and \textrm{rmse}_j its RMSE. If |s_{ij}| > L with threshold L, then x_{ij} is considered an outlier.

For large data sets, just by chance, many values can surpass the default threshold of 3. To reduce the number of outliers, the threshold can be increased. Alternatively, the number of outliers can be limited by the two arguments max_n_outliers and max_prop_outliers. For instance, if at most ten outliers are to be identified, set max_n_outliers = 10.

Since the random forest algorithm "ranger" does not allow for missing values, any missing value is first being imputed by chained random forests.

Value

An object of class "outForest" and a list with the following elements.

• Data: Original data set in unchanged row order but optionally with outliers replaced. Can be extracted with the Data() function.

• outliers: Compact representation of outliers, for details see the outliers() function used to extract them.

• n_outliers: Number of outliers per v.

• is_outlier: Logical matrix with outlier status. NULL if allow_predictions = FALSE.

• predData: data.frame with OOB predictions. NULL if allow_predictions = FALSE.

• allow_predictions: Same as allow_predictions.

• v: Variables checked.

• threshold: The threshold used.

• rmse: Named vector of RMSEs of the random forests. Used for scaling the difference between observed values and predicted.

• forests: Named list of fitted random forests. NULL if allow_predictions = FALSE.

• used_to_check: Variables used for checking v.

• mu: Named vector of sample means of the original v (incl. outliers).

References

1. Chandola V., Banerjee A., and Kumar V. (2009). Anomaly detection: A survey. ACM Comput. Surv. 41, 3, Article 15 <dx.doi.org/10.1145/1541880.1541882>.

2. Wright, M. N. & Ziegler, A. (2016). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, in press. <arxiv.org/abs/1508.04409>.

See Also

outliers(), Data() plot.outForest(), summary.outForest(), predict.outForest()

Examples

head(irisWithOut <- generateOutliers(iris, seed = 345))
(out <- outForest(irisWithOut))
outliers(out)
head(Data(out))
plot(out)
plot(out, what = "scores")

Extracts Outliers

Description

Extracts outliers from object of class "outForest". The outliers are sorted by their absolute score in descending fashion.

Usage

outliers(object, ...)

## Default S3 method:
outliers(object, ...)

## S3 method for class 'outForest'
outliers(object, ...)

Arguments

Value

A data.frame with one row per outlier. The columns are as follows:

• row, col: Row and column in original data with outlier.

• observed: Observed value.

• predicted: Predicted value.

• rmse: Scaling factor used to normalize the difference between observed and predicted.

• score: Outlier score defined as (observed-predicted)/RMSE.

• threshold: Threshold above which an outlier score counts as outlier.

• replacement: Value used to replace observed value.

Methods (by class)

• outliers(default): Default method not implemented yet.

• outliers(outForest): Extract outliers from outForest object.

Examples

x <- outForest(iris)
outliers(x)

Plots outForest

Description

This function can plot aspects of an "outForest" object.

• With what = "counts", the number of outliers per variable is visualized as a barplot.

• With what = "scores", outlier scores (i.e., the scaled difference between predicted and observed value) are shown as scatterplot per variable.

Usage

## S3 method for class 'outForest'
plot(x, what = c("counts", "scores"), ...)

Arguments

Value

A list.

Examples

irisWithOutliers <- generateOutliers(iris, seed = 345)
x <- outForest(irisWithOutliers, verbose = 0)
plot(x)
plot(x, what = "scores")

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 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

Value

An object of class "outForest".

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")

Prints outForest

Description

Print method for an object of class "outForest".

Usage

## S3 method for class 'outForest'
print(x, ...)

Arguments

Value

Invisibly, the input is returned.

Examples

x <- outForest(iris)
x

Summarizes outForest

Description

Summary method for an object of class "outForest". Besides the number of outliers per variables, it also shows the worst outliers.

Usage

## S3 method for class 'outForest'
summary(object, ...)

Arguments

Value

A list of summary statistics.

Examples

out <- outForest(iris, seed = 34, verbose = 0)
summary(out)