Find change points efficiently in linear regression models

fastcpd_lm() and fastcpd.lm() are wrapper functions of fastcpd() to find change points in linear regression models. The function is similar to fastcpd() except that the data is by default a matrix or data frame with the response variable as the first column and thus a formula is not required here.

Usage

fastcpd_lm(data, ...)

fastcpd.lm(data, ...)

Arguments

data

A matrix or a data frame with the response variable as the first column.

...

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)
  n <- 300
  p <- 4
  x <- mvtnorm::rmvnorm(n, rep(0, p), diag(p))
  theta_0 <- rbind(c(1, 3.2, -1, 0), c(-1, -0.5, 2.5, -2), c(0.8, 0, 1, 2))
  y <- c(
    x[1:100, ] %*% theta_0[1, ] + rnorm(100, 0, 3),
    x[101:200, ] %*% theta_0[2, ] + rnorm(100, 0, 3),
    x[201:n, ] %*% theta_0[3, ] + rnorm(100, 0, 3)
  )
  result_lm <- fastcpd.lm(cbind(y, x))
  summary(result_lm)
  plot(result_lm)
}
#> 
#> Call:
#> fastcpd.lm(data = cbind(y, x))
#> 
#> Change points:
#> 97 201 
#> 
#> Cost values:
#> 517.5695 412.9626 459.8569 
#> 
#> Parameters:
#>     segment 1  segment 2 segment 3
#> 1  0.74291290 -0.6153049 0.8733473
#> 2  3.69465275 -0.5034948 0.3222868
#> 3 -1.24746871  2.2522352 1.0188455
#> 4  0.09579985 -1.9875126 2.2761340

if (requireNamespace("mvtnorm", quietly = TRUE)) {
  set.seed(1)
  n <- 600
  p <- 4
  d <- 2
  x <- mvtnorm::rmvnorm(n, rep(0, p), diag(p))
  theta_1 <- matrix(runif(8, -3, -1), nrow = p)
  theta_2 <- matrix(runif(8, -1, 3), nrow = p)
  y <- rbind(
    x[1:350, ] %*% theta_1 + mvtnorm::rmvnorm(350, rep(0, d), 3 * diag(d)),
    x[351:n, ] %*% theta_2 + mvtnorm::rmvnorm(250, rep(0, d), 3 * diag(d))
  )
  result_mlm <- fastcpd.lm(cbind.data.frame(y = y, x = x), p.response = 2)
  summary(result_mlm)
}
#> 
#> Call:
#> fastcpd.lm(data = cbind.data.frame(y = y, x = x), p.response = 2)
#> 
#> Change points:
#> 350 
#> 
#> Cost values:
#> 1405.82 993.8667