posterior_estimate.ClustCpObj: Estimate the change points of the data

In BayesChange: Bayesian Methods for Change Points Analysis

Estimate the change points of the data

Description

The posterior_estimate method estimates the change points of the data making use of the salso algorithm, for a DetectCPObj class object.

Usage

## S3 method for class 'ClustCpObj'
posterior_estimate(
  object,
  loss = "VI",
  maxNClusters = 0,
  nRuns = 16,
  maxZealousAttempts = 10,
  ...
)

Arguments

object	an object of class ClustCpObj.
loss	The loss function used to estimate the final partition, it can be "VI", "binder", "omARI", "NVI", "ID", "NID".
maxNClusters	maximum number of clusters in salso procedure.
nRuns	number of runs in salso procedure.
maxZealousAttempts	maximum number of zealous attempts in salso procedure.
...	parameter of the generic method.

Details

put details here

Value

The function returns a vector with the cluster assignment of each observation.

References

#' D. B. Dahl, D. J. Johnson, and P. Müller (2022), Search Algorithms and Loss Functions for Bayesian Clustering, Journal of Computational and Graphical Statistics, 31(4), 1189-1201, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2022.2069779")}.

Examples

data_mat <- matrix(NA, nrow = 5, ncol = 100)

data_mat[1,] <- as.numeric(c(rnorm(50,0,0.100), rnorm(50,1,0.250)))
data_mat[2,] <- as.numeric(c(rnorm(50,0,0.125), rnorm(50,1,0.225)))
data_mat[3,] <- as.numeric(c(rnorm(50,0,0.175), rnorm(50,1,0.280)))
data_mat[4,] <- as.numeric(c(rnorm(25,0,0.135), rnorm(75,1,0.225)))
data_mat[5,] <- as.numeric(c(rnorm(25,0,0.155), rnorm(75,1,0.280)))

out <- clust_cp(data = data_mat, n_iterations = 5000, n_burnin = 1000,
                params = list(L = 1, B = 1000, phi = 0.5), kernel = "ts")

posterior_estimate(out)

BayesChange documentation built on June 10, 2025, 9:09 a.m.