ocd: ocd: A package for high-dimensional multiscale online...

In ocd: High-Dimensional Multiscale Online Changepoint Detection

Description

The ocd package provides the S3 class ChangepointDetector that processes data sequentially using the getData function and aims to detect change as soon as it occurs online subject to false alarm rates.

References

Chen, Y., Wang, T. and Samworth, R. J. (2020) High-dimensional multiscale online changepoint detection Preprint. arxiv:2003.03668.

See Also

ChangepointDetector for detailed usage.

Examples

set.seed(2020)
p <- 100
thresh <- setNames(c(11.62, 179.48, 54.87), c('diag', 'off_d', 'off_s'))
detector <- ChangepointDetector(dim=p, method='ocd', beta=1, thresh=thresh)
old_mean <- rnorm(p); new_mean <- old_mean + c(rnorm(p/4), rep(0,3*p/4)) / sqrt(p/4)

# using functional semantics native in R
detector <- setStatus(detector, 'estimating')
for (i in 1:10000){
  x_new <- rnorm(p, mean=old_mean)
  detector <- getData(detector, x_new)
}
print(detector)

detector <- setStatus(detector, 'monitoring')
for (i in 1:200){
  x_new <- rnorm(p, old_mean * (i < 100) + new_mean * (i>=100))
  detector <- getData(detector, x_new)
}
print(detector)

## Not run: 
# alternative way to write the above using the piping semantics
library(magrittr)
detector %<>% reset
detector %<>% setStatus('estimating')
for (i in 1:10000){
  x_new <- rnorm(p, mean=old_mean)
  detector %<>% getData(x_new)
}
detector %>% print

detector %<>% setStatus('monitoring')
for (i in 1:200){
  x_new <- rnorm(p, old_mean * (i < 100) + new_mean * (i>=100))
  detector %<>% getData(x_new)
}
detector %>% print

## End(Not run)

ocd documentation built on Oct. 23, 2020, 5:56 p.m.