Description Usage Arguments Details Value References Examples
Multiscale MOSUM procedure with (possibly) assymetric bandwidths and localised pruning based on Schwarz criterion.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  multiscale.localPrune(
x,
G = bandwidths.default(length(x)),
max.unbalance = 4,
threshold = c("critical.value", "custom")[1],
alpha = 0.1,
threshold.function = NULL,
criterion = c("eta", "epsilon")[1],
eta = 0.4,
epsilon = 0.2,
rule = c("pval", "jump")[1],
penalty = c("log", "polynomial")[1],
pen.exp = 1.01,
do.confint = FALSE,
level = 0.05,
N_reps = 1000,
...
)

x 
input data (a 
G 
a vector of bandwidths, given as either integers less than 
max.unbalance 
a numeric value for the maximal ratio between maximal and minimal bandwidths to be used for candidate generation,

threshold 
string indicating which threshold should be used to determine significance.
By default, it is chosen from the asymptotic distribution at the significance level 
alpha 
a numeric value for the significance level with

threshold.function 
function object of form 
criterion 
how to determine whether an exceeding point is a change point; to be parsed to mosum 
eta, epsilon 
see mosum 
rule 
string for the choice of sorting criterion for change point candidates in merging step. Possible values are:

penalty 
string specifying the type of penalty term to be used in Schwarz criterion; possible values are:

pen.exp 
exponent for the penalty term (see 
do.confint 
flag indicating whether confidence intervals for change points should be computed 
level 
use iff 
N_reps 
use iff 
... 
further arguments to be parsed to mosum calls 
See Algorithm 2 in the first referenced paper for a comprehensive description of the procedure and further details.
S3 object of class multiscale.cpts
, which contains the following fields:
x 
input data 
cpts 
estimated change points 
cpts.info 
data frame containing information about estimated change points 
sc 
Schwarz criterion values of the estimated change point set 
pooled.cpts 
set of change point candidates that have been considered by the algorithm 
G 
input parameter 
threshold, alpha, threshold.function 
input parameters 
criterion, eta, epsilon 
input parameters 
rule, penalty, pen.exp 
input parameters 
do.confint 
input parameter 
ci 
object of class 
A. Meier, C. Kirch and H. Cho (2021) mosum: A Package for Moving Sums in Changepoint Analysis. Journal of Statistical Software, Volume 97, Number 8, pp. 142. <doi:10.18637/jss.v097.i08>.
H. Cho and C. Kirch (2020) Twostage data segmentation permitting multiscale change points, heavy tails and dependence. arXiv preprint arXiv:1910.12486.
H. Cho and C. Kirch (2021) Bootstrap confidence intervals for multiple change points based on moving sum procedures. arXiv preprint arXiv:2106.12844.
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