BinSegBstrap {BinSegBstrap} | R Documentation |

## Estimates a piecewise smooth signal

### Description

A piecewise smooth signal is estimated by applying `BstrapTest`

recursively (binary segmentation approach). The final estimator is estimated by kernel smoothing on each segment separately; a joint bandwidth is selected by crossvalidation. More details can be found in the vignette.

### Usage

```
BinSegBstrap(y, bandwidth, nbandwidth = 30L, B = 500L, alpha = 0.05,
kernel = c("epanechnikov", "gaussian", "rectangular",
"triangular", "biweight", "silverman"))
```

### Arguments

`y` |
a numeric vector containing the data points |

`bandwidth` |
the bandwidth, i.e. a numeric with values between |

`nbandwidth` |
a single integer giving the number of bandwidths (see above) if |

`B` |
a single integer giving the number of bootstrap samples |

`alpha` |
a probability, i.e. a single numeric between 0 and 1, giving the significance level of the test |

`kernel` |
the kernel function, i.e. either a string or a function that takes a single numeric vector and returns the values of the kernel at those locations |

### Value

a `list`

with the following components:

- est: the estimated signal

- cps: the estimated change-point locations

- bandwidth: the selected bandwidth

### Examples

```
n <- 200
signal <- sin(2 * pi * 1:n / n)
signal[51:100] <- signal[51:100] + 5
signal[151:200] <- signal[151:200] + 5
y <- rnorm(n) + signal
# default bandwidth and kernel
est <- BinSegBstrap(y = y)
plot(y)
lines(signal)
lines(est$est, col = "red")
# fixed bandwidth
est <- BinSegBstrap(y = y, bandwidth = 0.1)
# user specified kernel
kernel <- function(x) 1 - abs(x) # triangular kernel
est <- BinSegBstrap(y = y, kernel = kernel)
```

*BinSegBstrap*version 1.0-1 Index]