gStream (version 0.2.0)

gstream: Sequential Change-Point Detection based on k-Nearest Neighbors

Description

This function finds change-points in the sequence when the underlying distribution changes. It reports four graph-based test statistics and the analytical approximations for thresholds used in their corresponding stopping rules.

Usage

gstream(distM, L, N0, k, statistics = c("all", "o", "w", "g", "m"),
n0 = 0.3*L, n1 = 0.7*L, ARL = 10000, alpha = 0.05, skew.corr = TRUE, asymp = FALSE)

Arguments

distM

A distance matrix constructed based on some distance measure.

L

The number of observations the k-NN graph will be constructed from.

N0

The number of historical observations.

k

A fixed integer used to construct k-NN graph.

statistics

The scan statistic to be computed. A character indicating the type of of scan statistic desired. The default is "all".

"all": specifies to compute all of the scan statistics: original, weighted, generalized, and max-type;

"o", "ori" or "original": specifies the original edge-count scan statistic;

"w" or "weighted": specifies the weighted edge-count scan statistic;

"g" or "generalized": specifies the generalized edge-count scan statistic; and

"m" or "max": specifies the max-type edge-count scan statistic.

n0

The starting index to be considered as a candidate for the change-point. We recommend you set this to be 0.2*L

n1

The ending index to be considered as a candidate for the change-point. For example, n1 = L-n0.

ARL

The average run length: the expectation of the stopping rule when there is no change-point.

alpha

The probability of an early stop.

skew.corr

Default is TRUE. If skew.corr is TRUE, the average run length approximation would incorporate skewness correction.

asymp

Default is FALSE. If asymp is TRUE, the average run length approximation will be based on the asymptotic analytical formulas.

Value

Returns a list with items scanZ, b and tauhat for each type of statistic specified. See below for more details.

scanZ

A vector of the test statistic (maximum of the scan statistics) for each time n = N0+1,..N.

ori: A vector of the original scan statistics (standardized counts) if statistic specified is "all" or "o".

weighted: A vector of the weighted scan statistics (standardized counts) if statistic specified is "all" or "w".

generalized: A vector of the generalized scan statistics (standardized counts) if statistic specified is "all" or "g".

max.type: A vector of the max-type scan statistics (standardized counts) if statistic specified is "all" or "m".

b

Thresholds used in the stopping rules for each test statistic. These thresholds are based on analytical approximations of the average run length.

tauhat

Estimate of the locations of change-points based on the thresholds.

See Also

gStream

Examples

# NOT RUN {
# This example contains two distance matrices (distM1 and distM2).
# Information on how distM1 and distM2 are generated can be found in gStream.

# data(Example)

# Example:
 # distM1 is a distance matrix constructed from a dataset with n=40 observation.
  # The first 20 observations are treated as historical observations.
  # It has been determined that there are no change-points among the
  # first 20 observations (see package gSeg for offline change-point detection).
  # There is change in mean when tau = 20 (This means a change happens 20 observations
  # after we start the tests. We start the test at N0+1 = 21.)

  # Uncomment the following to run
  # N0 = 20
  # L = 20 # the k-nn graph is constructed on only the L most recent observations.
  # k = 1

  # r1= gstream(distM1, L, N0, k, statistics="all", n0=0.3*L, n1=0.7*L,
  # ARL=200,alpha=0.05, skew.corr=TRUE, asymp=FALSE)
  # output results based on all four statistics; the scan statistics can be found in r1$scanZ
  # r1$tauhat # reports the locations where a change-point is detected
  # r1$b # reports the analytical approximations of the thresholds used in the stopping rules

  # Set ARL = 10,000
  # r1= gstream(distM1, L, N0, k, statistics="all", n0=0.3*L, n1=L-0.3*L,
  # ARL=10000,alpha=0.05, skew.corr=TRUE, asymp=FALSE) # uncomment to run this function
# }