Find change points efficiently in logistic regression models

fastcpd_binomial() and fastcpd.binomial() are wrapper functions of fastcpd() to find change points in logistic 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_binomial(data, ...)

fastcpd.binomial(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 <- 500
  p <- 4
  x <- mvtnorm::rmvnorm(n, rep(0, p), diag(p))
  theta <- rbind(rnorm(p, 0, 1), rnorm(p, 2, 1))
  y <- c(
    rbinom(300, 1, 1 / (1 + exp(-x[1:300, ] %*% theta[1, ]))),
    rbinom(200, 1, 1 / (1 + exp(-x[301:n, ] %*% theta[2, ])))
  )
  result <- suppressWarnings(fastcpd.binomial(cbind(y, x)))
  summary(result)
  plot(result)
}
#> 
#> Call:
#> fastcpd.binomial(data = cbind(y, x))
#> 
#> Change points:
#> 302 
#> 
#> Cost values:
#> 124.4554 54.26381 
#> 
#> Parameters:
#>    segment 1 segment 2
#> 1 -0.9260182 2.1294962
#> 2 -1.6033835 2.7583247
#> 3  1.0343338 2.3818010
#> 4  0.3653870 0.7261152