Description
Usage
Arguments
Value
References
Examples
Perform the Sparsified Binary Segmentation algorithm detecting changepoints in the mean or secondorder structure of the data.
 (x, cp.type = (1, 2)[1], thr = , trim = , = ,
temporal = , scales = , = , B = 1000, = 0.01,
do.parallel = 4)

x 
input data matrix, with each row representing the component time series

cp.type 
cp.type=1 specifies changepoints in the mean, cp.type=2 specifies changepoints in the secondorder structure

thr 
predefined threshold values; when thr = NULL , bootstrap procedure is employed for the threshold selection; when thr != NULL and cp.type = 1 , length(thr) should match nrow(x) , if cp.type = 2 , length(thr) should match nrow(x)*(nrow(x)+1)/2*length(scales)

trim 
length of the intervals trimmed off around the changepoint candidates; trim = NULL activates the default choice (trim = round(log(dim(x)[2])) )

height 
maximum height of the binary tree; height = NULL activates the default choice (height = floor(log(dim(x)[2], 2)/2) )

temporal 
used when cp.type = 1 ; if temporal = FALSE , rows of x are scaled by mad estimates, if temporal = TRUE , their longrun variance estimates are used

scales 
used when cp.type = 2 ; negative integers representing Haar wavelet scales to be used for computing nrow(x)*(nrow(x)+1)/2 dimensional wavelet transformation of x ; a small negative integer represents a fine scale

diag 
used when cp.type = 2 ; if diag = TRUE , only changes in the diagonal elements of the autocovariance matrices are searched for

B 
used when is.null(thr) ; number of bootstrap samples for threshold selection

q 
used when is.null(thr) ; quantile of bootstrap test statistics to be used for threshold selection

do.parallel 
used when is.null(thr) ; number of copies of R running in parallel, if do.parallel = 0 , %do% operator is used, see also foreach

S3 bin.tree
object, which contains the following fields:
tree 
a list object containing information about the nodes at which changepoints are detected

mat 
matrix concatenation of the nodes of tree

ecp 
estimated changepoints

thr 
threshold used to construct the tree

H. Cho and P. Fryzlewicz (2014) Multiplechangepoint detection for high dimensional time series via sparsified binary segmentation. JRSSB, vol. 77, pp. 475–507.
 x < ((20*300), =20)
(x, cp.type=2, scales=1, =, do.parallel=0)$ecp
x < ((100*300), =100)
x[1:10, 151:300] < x[1:10, 151:300]*(2)
(x, cp.type=2, scales=1, =, do.parallel=0)$ecp

hdbinseg documentation built on May 2, 2019, 9:13 a.m.