e.cp3o {ecp} | R Documentation |
An algorithm for multiple change point analysis that uses dynamic programming and pruning. The E-statistic is used as the goodness-of-fit measure.
e.cp3o(Z, K=1, minsize=30, alpha=1, verbose=FALSE)
Z |
A T x d matrix containing the length T time series with d-dimensional observations. |
K |
The maximum number of change points. |
minsize |
The minimum segment size. |
alpha |
The moment index used for determining the distance between and within segments. |
verbose |
A flag indicating if status updates should be printed. |
Segmentations are found through the use of dynamic programming and pruning. For long time series, consider using e.cp3o_delta.
The returned value is a list with the following components.
number |
The estimated number of change points. |
estimates |
The location of the change points estimated by the procedure. |
gofM |
A vector of goodness of fit values for differing number of change points. The first entry corresponds to when there is only a single change point, the second for when there are two, and so on. |
cpLoc |
The list of locations of change points estimated by the procedure for different numbers of change points up to K. |
time |
The total amount to time take to estimate the change point locations. |
Nicholas A. James
Rizzo M.L., Szekely G.L (2005). Hierarchical clustering via joint between-within distances: Extending ward's minimum variance method. Journal of Classification.
Rizzo M.L., Szekely G.L. (2010). Disco analysis: A nonparametric extension of analysis of variance. The Annals of Applied Statistics.
set.seed(400) x1 = matrix(c(rnorm(50),rnorm(50,3))) y1 = e.cp3o(Z=x1, K=2, minsize=30, alpha=1, verbose=FALSE) #View estimated change point locations y1$estimates