cpss.mean: Detecting changes in mean
In ghwang-nk/cpss: Change-Point Detection by Sample-Splitting Methods

cpss.mean

R Documentation

Detecting changes in mean

Description

Detecting changes in mean

Usage

cpss.mean(
  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,
  Sigma = NULL
)

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 is also acceptable for univariate observations.
`algorithm`	a character string specifying the change-point searching algorithm, one of the following choices: "SN" (segment neighborhood), "BS" (binary segmentation), "WBS" (wild binary segmentation) and "PELT" (pruned exact linear time) algorithms.
`dist_min`	an integer specifying minimum searching distance (length of feasible segments).
`ncps_max`	an integer specifying an upper bound of the number of true change-points.
`pelt_pen_val`	a numeric vector specifying candidate values of the penalty only if `algorithm = "PELT"`.
`pelt_K`	a numeric value for pruning adjustment only if `algorithm = "PELT"`. It is usually taken to be 0 if the negative log-likelihood is used as a cost, see Killick et al. (2012).
`wbs_nintervals`	an integer specifying the number of random intervals drawn only if `algorithm = "WBS"`, see Fryzlewicz (2014).
`criterion`	a character string specifying the model selection criterion, "CV" ("cross-validation") or "MS" ("multiple-splitting").
`times`	an integer specifying how many times of sample-splitting should be performed; It should be 2 if `criterion = "CV"`.
`Sigma`	if a numeric matrix (or constant) is supplied, it will be taken as the value of the common covariance (or variance). By default it is `NULL`, and the covariance is estimated by \widehat{Σ} = \frac{1}{2(n-1)}∑_{i=1}^{n-1} (Y_i-Y_{i+1})(Y_i-Y_{i+1})';

Value

cpss.mean 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.

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. Fryzlewicz, P. (2014). Wild binary segmentation for multiple change-point detection. The Annals of Statistics, 42(6): 2243–2281.

Examples

library("cpss")
set.seed(666)
n <- 2048
tau <- c(205, 267, 308, 472, 512, 820, 902, 1332, 1557, 1598, 1659)
seg_len <- diff(c(0, tau, n))
mu <- rep(c(0, 14.64, -3.66, 7.32, -7.32, 10.98, -4.39, 3.29, 19.03, 7.68, 15.37, 0), seg_len)
ep <- 7 * rnorm(n)
y <- mu + ep

res <- cpss.mean(y, algorithm = "SN", ncps_max = 20)
summary(res)
# 205  267  307  471  512  820  897  1332  1557  1601  1659
plot(res, type = "scatter")
plot(res, type = "path")
out <- update(res, dim_update = 12)
out@cps
# 205  267  307  471  512  820  897 1332 1557 1601 1659 1769
# coef(out)

ghwang-nk/cpss documentation built on Aug. 25, 2022, 8:12 a.m.