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Find mean changes in a univariate sequence

Source: R/hypothesis.R
uni_mean.Rd

The uni_mean() function ranks the observations from smallest to largest, then applies the pruned exact linear time algorithm with the penalty parameter beta to detect changepoints.

Usage

uni_mean(data, beta = 10)

Arguments

data

A vector or one-dimensional array.

beta

Numeric penalty constant passed to pruned exact linear time algorithm.

Value

A list consisting of:

  • $changepoints : Indices of the changepoints detected; will return integer(0) if no changepoints are detected.

  • $method : A string "Univariate Changepoint in Mean (FKWC)"

References

Killick, R., P. Fearnhead, and I. A. Eckley. “Optimal Detection of Changepoints With a Linear Computational Cost.” Journal of the American Statistical Association 107, no. 500 (2012): 1590–98. https://doi.org/10.1080/01621459.2012.737745.

Examples

set.seed(11)
mean_test <- c(rnorm(100, mean = 0), # before change in mean
               rnorm(100, mean = 5)) # after change in mean
uni_mean(mean_test)
#> $changepoints
#> [1] 100
#> 
#> $method
#> [1] "Univariate Changepoint in Mean (KWCChangepoint)"
#>