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, 10), rnorm(300, 0, 1))
system.time(result <- fastcpd.variance(data))
summary(result)
plot(result)
if (requireNamespace("mvtnorm", quietly = TRUE)) {
  set.seed(1)
  p <- 3
  data <- rbind(
    mvtnorm::rmvnorm(
      3e+5, rep(0, p), crossprod(matrix(runif(p^2) * 2 - 1, p))
    ),
    mvtnorm::rmvnorm(
      4e+5, rep(0, p), crossprod(matrix(runif(p^2) * 2 - 1, p))
    ),
    mvtnorm::rmvnorm(
      3e+5, rep(0, p), crossprod(matrix(runif(p^2) * 2 - 1, p))
    )
  )
  result_time <- system.time(
    result <- fastcpd.variance(data, r.progress = FALSE, cp_only = TRUE)
  )
  print(result_time)
  summary(result)
}

fastcpd documentation built on April 12, 2025, 1:43 a.m.