segment {segmentr}

Segment data into change points

Description

Generic function to segment data into separate change points according to specified algorithm

Usage

segment(data, likelihood, max_segments = ncol(data),
  allow_parallel = TRUE, algorithm = "exact", ...)

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.

algorithm

can be of type exact, hierarchical or hybrid, Default: exact

...

other parameters to be passed to the underlying function

Details

This function can be used as a generic function to call any of the algorithms implemented by the package. Depending on the type of data the user wants to segment, one algorithm might be more adequate than the others.

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.

See Also

exactalg() for the exact algorithm, hieralg() for the hierarchical algorithm implementation, hybridalg() for the hybrid algorithm implementation.

Examples

make_segment <- function(n, p) matrix(rbinom(100 * n, 1, p), nrow = 100)
data <- cbind(make_segment(5, 0.1), make_segment(10, 0.9), make_segment(2, 0.1))
mean_lik <- function(X) abs(mean(X) - 0.5) * ncol(X)^2
segment(data, likelihood = mean_lik, algorithm = "hieralg")