hybridalg: Segment data into change points using a mixed...

In segmentr: Segment Data With Maximum Likelihood

Description

For the larger datasets, assume the data is hierarchical, but calculate the exact segments when they're smaller than a threshold

Usage

hybridalg(data, likelihood, allow_parallel = TRUE,
  max_segments = ncol(data), threshold = 50)

Arguments

data	matrix for which to find the change points
likelihood	a function receives the segment matrix as argument and returns a likelihood estimation. This function is used to calculate the change points that maximize the total likelihood. Depending on the algorithm being used, this function is likely to be executed many times, in which case it's also likely to be the bottleneck of the function execution, so it's advised that this function should have fast implementation.
allow_parallel	allows parallel execution to take place using the registered cluster. Assumes a cluster is registered with the foreach package. Defaults to TRUE.
max_segments	an integer that defines the maximum amount of segments to split the data into.
threshold	the threshold for which the exact algorithm will be used, i.e. when the number of columns in the segment is less than or equal to the threshold.

Details

This algorithm implements an approach mixing the hierarchical and exact algorithms. It uses the hierarchical algorithms when the size of the segment is bigger than the threshold, and then goes on to use the exact algorithm when the size of the segment is less than or equal to the threshold.

Value

a list of type segmentr, which has the two attributes:

• changepoints: a vector with the first index of each identified change point

• segments: a list of vectors, in which each vector corresponds to the indices that identifies a segment.

segmentr documentation built on Aug. 29, 2019, 1:03 a.m.