fastcpd_variance: Find change points efficiently in variance change models

In fastcpd: Fast Change Point Detection via Sequential Gradient Descent

Find change points efficiently in variance change models

Description

fastcpd_variance() and fastcpd.variance() are wrapper functions of fastcpd() to find the variance change. The function is similar to fastcpd() except that the data is by default a matrix or data frame or a vector with each row / element as an observation and thus a formula is not required here.

Usage

fastcpd_variance(data, ...)

fastcpd.variance(data, ...)

Arguments

data	A matrix, a data frame or a vector.
...	Other arguments passed to fastcpd(), for example, segment_count.

Value

A fastcpd object.

See Also

fastcpd()

Examples

set.seed(1)
data <- c(rnorm(300, 0, 1), rnorm(400, 0, 100), rnorm(300, 0, 1))
result <- fastcpd.variance(data)
summary(result)
if (requireNamespace("mvtnorm", quietly = TRUE)) {
  set.seed(1)
  p <- 3
  result <- fastcpd.variance(
    rbind(
      mvtnorm::rmvnorm(
        300, rep(0, p), crossprod(matrix(runif(p^2) * 2 - 1, p))
      ),
      mvtnorm::rmvnorm(
        400, rep(0, p), crossprod(matrix(runif(p^2) * 2 - 1, p))
      ),
      mvtnorm::rmvnorm(
        300, rep(0, p), crossprod(matrix(runif(p^2) * 2 - 1, p))
      )
    )
  )
  summary(result)
}

set.seed(1)
data <- c(rnorm(3000, 0, 1), rnorm(3000, 0, 2), rnorm(3000, 0, 1))
(result_time <- system.time(
  result <- fastcpd.variance(data, r.progress = FALSE, cp_only = TRUE)
))
result@cp_set


set.seed(1)
data <- c(rnorm(3000, 0, 1), rnorm(3000, 0, 2), rnorm(3000, 0, 1))
(result_time <- system.time(
  result <- fastcpd.variance(
    data, beta = "BIC", cost_adjustment = "BIC",
    r.progress = FALSE, cp_only = TRUE
  )
))
result@cp_set

fastcpd documentation built on May 29, 2024, 8:36 a.m.