fastcpd_mean: Find change points efficiently in mean change models

In fastcpd: Fast Change Point Detection via Sequential Gradient Descent

Find change points efficiently in mean change models

Description

fastcpd_mean() and fastcpd.mean() are wrapper functions of fastcpd() to find the mean 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_mean(data, ...)

fastcpd.mean(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 <- matrix(c(
  rnorm(300, mean = 0, sd = 10),
  rnorm(400, mean = 50, sd = 10),
  rnorm(300, mean = 2, sd = 10)
))
system.time(result <- fastcpd.mean(data))
summary(result)
plot(result)
set.seed(1)
p <- 3
data <- rbind(
  matrix(rnorm(p * 3e+5, mean = 0, sd = 10), ncol = p),
  matrix(rnorm(p * 4e+5, mean = 50, sd = 10), ncol = p),
  matrix(rnorm(p * 3e+5, mean = 2, sd = 10), ncol = p)
)
system.time(result <- fastcpd.mean(data, r.progress = FALSE, cp_only = TRUE))
summary(result)

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