The binsegrcppmeanvar from diego-urgell

binsegrcppmeanvar's Introduction

binsegRcpp Efficient implementation of the binary segmentation heuristic algorithm for changepoint detection, using C++ std::multimap.

tests
coverage

Installation

install.packages("binsegRcpp")
##OR
if(require("remotes"))install.packages("remotes")
remotes::install_github("tdhock/binsegRcpp")

Usage

The main function is binseg_normal which takes arguments:

data.vec is a numeric vector of data to segment.
max.segments is a positive integer, the maximum number of segments to consider (by default equal to the number of data points).

> x <- c(0.1, 0, 1, 1.1, 0.1, 0)
> (models.dt <- binsegRcpp::binseg_normal(x))
   segments         loss end before.mean after.mean before.size after.size
1:        1 1.348333e+00   6   0.3833333         NA           6         NA
2:        2 1.015000e+00   4   0.5500000       0.05           4          2
3:        3 1.500000e-02   2   0.0500000       1.05           2          2
4:        4 1.000000e-02   5   0.1000000       0.00           1          1
5:        5 5.000000e-03   1   0.1000000       0.00           1          1
6:        6 4.440892e-16   3   1.0000000       1.10           1          1
   invalidates.index invalidates.after
1:                NA                NA
2:                 1                 0
3:                 2                 0
4:                 2                 1
5:                 3                 0
6:                 3                 1

The table above contains all of the data that are computed during the binary segmentation algorithm. It has a special class with dedicated methods:

> class(models.dt)
[1] "binseg_normal" "data.table"    "data.frame"   
> methods(class="binseg_normal")
[1] coef plot
see '?methods' for accessing help and source code

The coef methods returns a data table of segment means:

> coef(models.dt, segments=2:3)
   segments start end mean
1:        2     1   4 0.55
2:        2     5   6 0.05
3:        3     1   2 0.05
4:        3     3   4 1.05
5:        3     5   6 0.05