cpss.meanvar: Detecting changes in mean and (co)variance

Description

Detecting changes in mean and (co)variance

Usage

cpss.meanvar(
  dataset,
  algorithm = "BS",
  dist_min = floor(log(n)),
  ncps_max = ceiling(n^0.4),
  pelt_pen_val = NULL,
  pelt_K = 0,
  wbs_nintervals = 500,
  criterion = "CV",
  times = 2
)

Value

cpss.meanvar returns an object of an S4 class, called "cpss", which collects data and information required for further change-point analyses and summaries. See cpss.custom.

Arguments

dataset: a numeric matrix of dimension \(n\times d\), where each row represents an observation and each column stands for a variable. A numeric vector could also be acceptable for univariate observations.
algorithm: a character string specifying the change-point searching algorithm, one of four state-of-the-art candidates "SN" (segment neighborhood), "BS" (binary segmentation), "WBS" (wild binary segmentation) and "PELT" (pruned exact linear time) algorithms.
dist_min: an integer indicating the minimum distance between two successive candidate change-points, with a default value \(floor(log(n))\).
ncps_max: an integer indicating the maximum number of change-points searched for, with a default value \(ceiling(n^0.4)\).
pelt_pen_val: a numeric vector specifying the collection of candidate values of the penalty if the "PELT" algorithm is used.
pelt_K: a numeric value to adjust the pruning tactic, usually is taken to be 0 if negative log-likelihood is used as a cost; more details can be found in Killick et al. (2012).
wbs_nintervals: an integer indicating the number of random intervals drawn in the "WBS" algorithm and a default value 500 is used.
criterion: a character string indicating which model selection criterion, "cross- validation" ("CV") or "multiple-splitting" ("MS"), is used.
times: an integer indicating how many times of sample-splitting should be performed; if "CV" criterion is used, it should be set as 2.

References

Killick, R., Fearnhead, P., and Eckley, I. A. (2012). Optimal Detection of Changepoints With a Linear Computational Cost. Journal of the American Statistical Association, 107(500):1590–1598.

Examples

Run this code

library("cpss")
if (!requireNamespace("MASS", quietly = TRUE)) {
  stop("Please install the package \"MASS\".")
}
set.seed(666)
n <- 1000
tau <- c(200, 400, 600, 800)
mu <- list(rep(0, 2), rep(1, 2), rep(1, 2), rep(0, 2), rep(0, 2))
Sigma <- list(diag(2), diag(2), matrix(c(1,-1,-1, 4), 2), matrix(c(1, 0.5, 0.5, 1), 2), diag(2))
seg_len <- diff(c(0, tau, n))
y <- lapply(seq(1, length(tau) + 1), function(k) {
  MASS::mvrnorm(n = seg_len[k], mu = mu[[k]], Sigma = Sigma[[k]])
})
y <- do.call(rbind, y)
res <- cpss.meanvar(y, algorithm = "BS", dist_min = 20)
cps(res)
# [1] 211 402 598 804
plot(res, type = "coef")