bwd {bwd} | R Documentation |

## Backward procedure for the change point detection

### Description

Implements backward procedure for detecting single or multiple change points.

### Usage

```
bwd(y, alpha = 0.05, kmin = 3, lastkgroup = floor(0.01 * n),
mu0 = NULL, normal = T, n.permute = 1000, h = 10)
```

### Arguments

`y` |
observed data |

`alpha` |
target level that detemines stopping criterion. Default is 0.05 |

`kmin` |
minimum length of segements for checking possible change points |

`lastkgroup` |
We can abvoid chekcing possible change points when we have less groups than "lastkgroup" to improve computational efficiency. Default is 0.01 * n |

`mu0` |
Baseline mean value whe detecting epidemic chang points. Defalut is |

`normal` |
if |

`n.permute` |
number of permutation when computing the permuted cutoff. Defalut is 1000 |

`h` |
bandwidth size for variance esitimator |

### Value

bwd object that contains information of detected segments and significance levels

### Author(s)

Seung Jun Shin, Yicaho Wu, Ning Hao

### References

Shin, Wu, and Hao (2018+) A backward procedure for change-point detection with applications to copy number variation detection, arXiv:1812.10107.

### See Also

### Examples

```
# simulated data
set.seed(1)
n <- 1000
L <- 10
mu0 <- -0.5
mu <- rep(mu0, n)
mu[(n/2 + 1):(n/2 + L)] <- mu0 + 1.6
mu[(n/4 + 1):(n/4 + L)] <- mu0 - 1.6
y <- mu + rnorm(n)
alpha <- c(0.01, 0.05)
# BWD
obj1 <- bwd(y, alpha = alpha)
# Modified for epidemic changes with a known basline mean, mu0.
obj2 <- bwd(y, alpha = alpha, mu0 = 0)
par(mfrow = c(2,1))
plot(obj1, y)
plot(obj2, y)
```

*bwd*version 0.1.0 Index]