exactalg: Segment data into exact change points

In segmentr: Segment Data With Maximum Likelihood

Description

Find changes points in data calculating the penalized likelihood for all possible segment combinations

Usage

exactalg(data, likelihood, max_segments = ncol(data),
  allow_parallel = TRUE)

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.
max_segments	an integer that defines the maximum amount of segments to split the data into.
allow_parallel	allows parallel execution to take place using the registered cluster. Assumes a cluster is registered with the foreach package. Defaults to TRUE.

Details

Function that implements the dynamic programming algorithm, with the intent of finding points of independent change points for which the likelihood function is maximized. It analyzes all possible combinations, returning the change points that are guaranteed to segment the data matrix in the maximum likelihood independent change points. Because it analyzes all possible combinations of change points, it has a O-squared algorithm complexity, meaning it works in an acceptable computation time for small datasets, but it takes quite longer for datasets with many columns. For big datasets, hieralg() might be more adequate.

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.