DP.SEPP {changepoints} | R Documentation |
l_0
penalty.Perform dynamic programming for SEPP change points detection.
DP.SEPP(DATA, gamma, lambda, delta, delta2, intercept, threshold)
DATA |
A |
gamma |
A |
lambda |
A |
delta |
An |
delta2 |
An |
intercept |
A |
threshold |
A |
An object of class
"DP", which is a list
with the following structure:
partition |
A vector of the best partition. |
cpt |
A vector of change points estimation. |
Daren Wang & Haotian Xu
Wang, D., Yu, Y., & Willett, R. (2020). Detecting Abrupt Changes in High-Dimensional Self-Exciting Poisson Processes. arXiv preprint arXiv:2006.03572.
p = 8 # dimension
n = 15
s = 3 # s is sparsity
factor = 0.2 # large factor gives exact recovery
threshold = 4 # thresholding makes the process stable
intercept = 1/2 # intercept of the model. Assume to be known as in the existing literature
A1 = A2 = A3 = matrix(0, p, p)
diag(A1[,-1]) = 1
diag(A1) = 1
diag(A1[-1,]) = -1
A1 = A1*factor
A1[(s+1):p, (s+1):p] = 0
diag(A2[,-1]) = 1
diag(A2) = -1
diag(A2[-1,]) = 1
A2 = A2*factor
A2[(s+1):p, (s+1):p] = 0
data1 = simu.SEPP(intercept, n, A1, threshold, vzero = NULL)
data2 = simu.SEPP(intercept, n, A2, threshold, vzero = data1[,n])
data = cbind(data1, data2)
gamma = 0.1
delta = 0.5*n
delta2 = 1.5*n
intercept = 1/2
threshold = 6
DP_result = DP.SEPP(data, gamma = gamma, lambda = 0.03, delta, delta2, intercept, threshold)
cpt_hat = DP_result$cpt