new_OCD: constructor of subclass 'OCD' in class 'ChangepointDetector'

In ocd: High-Dimensional Multiscale Online Changepoint Detection

Description

constructor of subclass 'OCD' in class 'ChangepointDetector'

Usage

new_OCD(dim, thresh, beta, sparsity)

Arguments

dim	Data dimension, all new data must be of this dimension
thresh	A numeric vector of length 3 (corresponding to the diagonal statistic, off-diagonal dense statistic and off-diagonal sparse statistic) should be specifiied.
beta	Lower bound on the l_2 norm of the vector of mean change to be detected.
sparsity	If sparsity='sparse', then only the diagonal and off-diagonal sparse statistics are used. If sparsity='dense', then only the diagonal and off-diagonal sparse statistics are used. If sparsity='auto', all three statistics are used to detect both sparse and dense change adaptively.

Details

It is preferred to use ChangepointDetector for construction.

Value

An object of S3 subclass 'OCD' in class 'ChangepointDetector'.

References

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

Examples

detector <- new_OCD(dim=100, thresh=c(11.6, 179.5, 54.9), beta=1, sparsity='auto')

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