SKFCPD-class {SKFCPD}

Class "SKFCPD"

Description

S4 class for SKFCPD where the range parameter and noise-to-signal parameters are estimated from the training samples.

Objects from the Class

Objects of this class are created and initialized with the function SKFCPD that computes the calculations needed for setting up the analysis.

Slots

design:

Object of class "matrix" with dimension n x p. The design of the experiment.

response:

Object of class "matrix" with dimension n x q. The observations.

test_start:

Object of class "numeric". The starting index of test period.

kernel_type:

Object of class "character" to specify the type of kernel to use.

gamma:

Object of class "vector" with dimension q x 1. The range parameters.

eta:

Object of class "vector" with dimension q x 1. The noise-to-signal ratio.

sigma_2:

Object of class "vector" with dimension q x 1. The variance parameters.

hazard_vec:

Object of class "numeric". The n x 1 hazard vector in the FastCPD method.

KF_params_list:

Object of class "list". The list of Kalman filter parameters from the previous run of the algorithm.

prev_L_params_list:

Object of class "list". The list of parameters for calculating the quadratic form of the inverse covariance matrix from the previous run of the algorithm.

run_length_posterior_mat:

Object of class "matrix" with dimension n x n. The posterior distribution of the run length.

run_length_joint_mat:

Object of class "matrix" with dimension n x n. The joint distribution of the run length and the observations.

log_pred_dist_mat:

Object of class "matrix" with dimension n x n. The logrithm of the predictive distribution of observations.

cp:

Object of class "vector" with length m. The location of estimated changepoints.

Author(s)

Hanmo Li [aut, cre], Yuedong Wang [aut], Mengyang Gu [aut]

Maintainer: Hanmo Li <[e-mail]>

References

Li, Hanmo, Yuedong Wang, and Mengyang Gu. Sequential Kalman filter for fast online changepoint detection in longitudinal health records. arXiv preprint arXiv:2310.18611 (2023).

Fearnhead, Paul, and Zhen Liu. On-line inference for multiple changepoint problems. Journal of the Royal Statistical Society Series B: Statistical Methodology 69, no. 4 (2007): 589-605.

Adams, Ryan Prescott, and David JC MacKay. Bayesian online changepoint detection. arXiv preprint arXiv:0710.3742 (2007).

Hartikainen, Jouni, and Simo Sarkka. Kalman filtering and smoothing solutions to temporal Gaussian process regression models. In 2010 IEEE international workshop on machine learning for signal processing, pp. 379-384. IEEE, 2010.

See Also

SKFCPD for more details about how to create a SKFCPD object.