pass: Detection of multivariate anomalous segments using PASS.

Description

Implements the PASS (Proportion Adaptive Segment Selection) procedure of Jeng et al. (2012). PASS uses a higher criticism statistic to pool the information about the presence or absence of a collective anomaly across the components. It uses Circular Binary Segmentation to detect multiple collective anomalies.

Usage

pass(x, alpha = 2, lambda = NULL, max_seg_len = 10, min_seg_len = 1)

Value

An instance of an S4 object of type .pass.class containing the data X, procedure parameter values, and the results.

Arguments

x: A numeric matrix with n rows and p columns containing the data which is to be inspected. The time series data classes ts, xts, and zoo are also supported.
alpha: A positive integer > 0. This value is used to stabilise the higher criticism based test statistic used by PASS leading to a better finite sample familywise error rate. Anomalies affecting fewer than alpha components will however in all likelihood escape detection. The default is 2.
lambda: A positive real value setting the threshold value for the familywise Type 1 error. The default value is \((1.1 {\rm log}(n \times max\_seg\_len) +2 {\rm log}({\rm log}(p))) / \sqrt{{\rm log}({\rm log}(p))}\).
max_seg_len: A positive integer (max_seg_len > 0) corresponding to the maximum interval length. This parameter corresponds to L in Jeng et al. (2012). The default value is 10.
min_seg_len: A positive integer (max_seg_len >= min_seg_len > 0) corresponding to the minimum segment length. The default value is 1.

References

10.1093/biomet/ass059anomaly

Examples

Run this code

library(anomaly)
# generate some multivariate data
data(simulated)
res<-pass(sim.data)
summary(res)
plot(res,variate_names=TRUE)

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