# sbs: Change-point detection via standard Binary Segmentation In wbs: Wild Binary Segmentation for Multiple Change-Point Detection

## Description

The function applies the Binary Segmentation algorithm to identify potential locations of the change-points in the mean of the input vector `x`. The object returned by this routine can be further passed to the `changepoints` function, which finds the final estimate of the change-points based on thresholding.

## Usage

 ```1 2 3 4``` ```sbs(x, ...) ## Default S3 method: sbs(x, ...) ```

## Arguments

 `x` a numeric vector `...` not in use

## Value

an object of class "sbs", which contains the following fields

 `x` the vector provided `n` the length of `x` `res` a 6-column matrix with results, where 's' and 'e' denote start- end points of the intervals in which change-points candidates 'cpt' have been found; column 'CUSUM' contains corresponding value of CUSUM statistic; 'min.th' is the smallest threshold value for which given change-point candidate would be not added to the set of estimated change-points; the last column is the scale at which the change-point has been found

## Examples

 ```1 2 3 4 5 6 7``` ```x <- rnorm(300) + c(rep(1,50),rep(0,250)) s <- sbs(x) s.cpt <- changepoints(s) s.cpt th <- c(s.cpt\$th,0.7*s.cpt\$th) s.cpt <- changepoints(s,th=th) s.cpt ```

### Example output

```\$sigma
[1] 0.903994

\$th
[1] 3.969222

\$no.cpt.th
[1] 1

\$cpt.th
\$cpt.th[[1]]
[1] 45

\$Kmax
[1] 1

\$sigma
[1] 0.903994

\$th
[1] 2.778455 3.969222

\$no.cpt.th
[1] 1 1

\$cpt.th
\$cpt.th[[1]]
[1] 45

\$cpt.th[[2]]
[1] 45

\$Kmax
[1] 1
```

wbs documentation built on May 30, 2017, 3:56 a.m.