Find change points efficiently in exponentially distributed data

fastcpd_exponential() and fastcpd.exponential() are wrapper functions of fastcpd() to find changes in the rate of exponentially distributed data, i.e. mean change under exponentially distributed noise (cf. changepoint::cpt.meanvar with test.stat = "Exponential"). 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_exponential(data, ...)

fastcpd.exponential(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(
  rexp(300, rate = 1),
  rexp(400, rate = 0.1),
  rexp(300, rate = 1)
))
system.time(result <- fastcpd.exponential(data))
#>    user  system elapsed 
#>   0.004   0.000   0.003 
summary(result)
#> 
#> Call:
#> fastcpd.exponential(data = data)
#> 
#> Change points:
#> 300 700 
#> 
#> Cost values:
#> 301.6378 1326.448 331.5256 
#> 
#> Parameters:
#>   segment 1  segment 2 segment 3
#> 1  1.004055 0.09939726 0.9088465
plot(result)