# GetBestBreak: Find most likely change point in irregular time series In bcpa: Behavioral Change Point Analysis of Animal Movement

 GetBestBreak R Documentation

## Find most likely change point in irregular time series

### Description

Finds the single best change point according to the likelihood function. Used internally within `WindowSweep`.

### Usage

```GetBestBreak(x, t, range = 0.6, ...)
```

### Arguments

 `x` vector of time series values. `t` vector of times of measurements associated with x. `range` of possible breaks. Default (0.6) runs approximately from 1/5 to 4/5 of the total length of the time series. `...` additional parameters to pass to `GetDoubleL` function.

### Value

returns a single row (vector) with elements: `breaks`,`tbreaks`,`mu1`,`sigma1`,`rho1`,`LL1`,`mu2`,`sigma2`,`rho2`,`LL2`,`LL`. The breakpoint is calculated for a range of possible values of width `range*l` (where `l` is the length of the time series). The output of this function feeds `WindowSweep`.

### Author(s)

Eliezer Gurarie

`WindowSweep` which uses it, and `GetDoubleL` for the likelihood estimation.

### Examples

```# An example with a single break:
x <- c(arima.sim(list(ar = 0.9), 20) + 10, arima.sim(list(ar = 0.1), 20))
t <- 1:length(x)
plot(t,x, type="l")
(bb <- GetBestBreak(x,t, tau=FALSE))
abline(v = bb, col=2)
```

bcpa documentation built on May 30, 2022, 5:07 p.m.