Find change points efficiently in mean change models

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

if (requireNamespace("mvtnorm", quietly = TRUE)) {
  set.seed(1)
  p <- 3
  data <- rbind(
    mvtnorm::rmvnorm(300, mean = rep(0, p), sigma = diag(100, p)),
    mvtnorm::rmvnorm(400, mean = rep(50, p), sigma = diag(100, p)),
    mvtnorm::rmvnorm(300, mean = rep(2, p), sigma = diag(100, p))
  )
  result <- fastcpd.mean(data)
  summary(result)
}
#> 
#> Call:
#> fastcpd.mean(data = data)
#> 
#> Change points:
#> 300 700 
#> 
#> Cost values:
#> 443.9553 597.9797 437.3706 
set.seed(1)
data <- c(rnorm(10000), rnorm(1000) + 1)
(result_time <- system.time(
  result <- fastcpd.mean(data, r.progress = FALSE, cp_only = TRUE)
))
#>    user  system elapsed 
#>   0.069   0.090   0.060 
result@cp_set
#> [1] 10007
# \donttest{
set.seed(1)
data <- c(rnorm(10000), rnorm(10000, 1), rnorm(10000))
(result_time <- system.time(
  result <- fastcpd.mean(data, r.progress = FALSE, cp_only = TRUE)
))
#>    user  system elapsed 
#>   0.336   0.282   0.289 
result@cp_set
#> [1] 10007 20002
# }
# \donttest{
set.seed(1)
data <- c(rnorm(10000), rnorm(10000, 1), rnorm(10000))
(result_time <- system.time(
  result <- fastcpd.mean(
    data, beta = "BIC", cost_adjustment = "BIC",
    r.progress = FALSE, cp_only = TRUE
  )
))
#>    user  system elapsed 
#>  83.710   0.275  83.675 
result@cp_set
#> [1] 10006 20000
# }