WBS.uni.nonpar {changepoints} R Documentation

## Wild binary segmentation for univariate nonparametric change points detection.

### Description

Perform wild binary segmentation for univariate nonparametric change points detection.

### Usage

WBS.uni.nonpar(Y, s, e, Alpha, Beta, N, delta, level = 0)


### Arguments

 Y A numeric matrix of observations with horizontal axis being time, and vertical axis being multiple observations on each time point. s A integer scalar of starting index. e A integer scalar of ending index. Alpha A integer vector of starting indices of random intervals. Beta A integer vector of ending indices of random intervals. N A integer vector representing number of multiple observations on each time point. delta A positive integer scalar of minimum spacing. level Should be fixed as 0.

### Value

A list with the following structure:

 S A vector of estimated change points (sorted in strictly increasing order) Dval A vector of values of CUSUM statistic based on KS distance Level A vector representing the levels at which each change point is detected Parent A matrix with the starting indices on the first row and the ending indices on the second row

### References

Padilla, Yu, Wang and Rinaldo (2021) <doi:10.1214/21-EJS1809>.

thresholdBS for obtaining change points estimation, tuneBSuninonpar for a tuning version.

### Examples

Y = t(as.matrix(c(rnorm(100, 0, 1), rnorm(100, 0, 10), rnorm(100, 0, 40))))
N = rep(1, 300)
M = 20
intervals = WBS.intervals(M = M, lower = 1, upper = ncol(Y))
temp = WBS.uni.nonpar(Y, 1, 300, intervals$Alpha, intervals$Beta, N, 5)
plot.ts(t(Y))
points(x = tail(temp$S[order(temp$Dval)], 4), y = Y[,tail(temp$S[order(temp$Dval)],4)], col = "red")
thresholdBS(temp, 2)


[Package changepoints version 1.1.0 Index]