cpss.mean {cpss}

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{\Sigma} = \frac{1}{2(n-1)}\sum_{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.

See Also

cpss.meanvar cpss.var

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)