Find change points efficiently in mean variance change models

fastcpd_meanvariance(), fastcpd.meanvariance(), fastcpd_mv(), fastcpd.mv() are wrapper functions of fastcpd() to find the meanvariance 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_meanvariance(data, ...)

fastcpd.meanvariance(data, ...)

fastcpd_mv(data, ...)

fastcpd.mv(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)
p <- 1
result <- fastcpd.meanvariance(c(
  rnorm(300, 0, 1),
  rnorm(400, 10, 1),
  rnorm(300, 0, 10),
  rnorm(300, 0, 1),
  rnorm(400, 10, 1),
  rnorm(300, 10, 10)
))
summary(result)
#> 
#> Call:
#> fastcpd.meanvariance(data = c(rnorm(300, 0, 1), rnorm(400, 10, 
#>     1), rnorm(300, 0, 10), rnorm(300, 0, 1), rnorm(400, 10, 1), 
#>     rnorm(300, 10, 10)))
#> 
#> Change points:
#> 300 700 1001 1300 1700 
#> 
#> Cost values:
#> -5.890901 26.91692 720.0313 17.3074 2.232584 724.8531 
plot(result)

if (requireNamespace("mvtnorm", quietly = TRUE)) {
  set.seed(1)
  p <- 3
  result <- fastcpd.mv(
    rbind(
      mvtnorm::rmvnorm(600, mean = rep(0, p), sigma = diag(1, p)),
      mvtnorm::rmvnorm(800, mean = rep(100, p), sigma = diag(1, p)),
      mvtnorm::rmvnorm(600, mean = rep(0, p), sigma = diag(900, p)),
      mvtnorm::rmvnorm(600, mean = rep(0, p), sigma = diag(1, p)),
      mvtnorm::rmvnorm(800, mean = rep(100, p), sigma = diag(1, p)),
      mvtnorm::rmvnorm(600, mean = rep(100, p), sigma = diag(900, p))
    )
  )
  summary(result)
}
#> 
#> Call:
#> fastcpd.mv(data = rbind(mvtnorm::rmvnorm(600, mean = rep(0, p), 
#>     sigma = diag(1, p)), mvtnorm::rmvnorm(800, mean = rep(100, 
#>     p), sigma = diag(1, p)), mvtnorm::rmvnorm(600, mean = rep(0, 
#>     p), sigma = diag(900, p)), mvtnorm::rmvnorm(600, mean = rep(0, 
#>     p), sigma = diag(1, p)), mvtnorm::rmvnorm(800, mean = rep(100, 
#>     p), sigma = diag(1, p)), mvtnorm::rmvnorm(600, mean = rep(100, 
#>     p), sigma = diag(900, p))))
#> 
#> Change points:
#> 600 1400 2003 2600 3400 
#> 
#> Cost values:
#> 98.15034 113.8909 6155.912 68.40162 -17.3152 6146.93 
# \donttest{
set.seed(1)
data <- c(rnorm(2000, 0, 1), rnorm(2000, 1, 1), rnorm(2000, 1, 2))
(result_time <- system.time(
  result <- fastcpd.meanvariance(data, r.progress = FALSE, cp_only = TRUE)
))
#>    user  system elapsed 
#>    0.02    0.00    0.02 
result@cp_set
#> [1] 2032 4003
# }
# \donttest{
set.seed(1)
data <- c(rnorm(2000, 0, 1), rnorm(2000, 1, 1), rnorm(2000, 1, 2))
(result_time <- system.time(
  result <- fastcpd.meanvariance(
    data, beta = "BIC", cost_adjustment = "BIC",
    r.progress = TRUE, cp_only = TRUE
  )
))
#>    user  system elapsed 
#>   0.377   0.000   0.378 
result@cp_set
#> [1] 2002 4000
# }