detectGlasso {detectR}

Change point detection using Graphical lasso as in Cribben et al. (2012)

Description

This function implements the Dynamic Connectivity Regression (DCR) algorithm proposed by Cribben el al. (2012) to locate changepoints.

Usage

detectGlasso(
  Y,
  Del,
  p,
  lambda = "bic",
  nboot = 100,
  n.cl,
  bound = c(0.001, 1),
  gridTF = FALSE,
  plotTF = TRUE
)

Arguments

Y

Input data of dimension length*dim (T times d)

Del

Delta away from the boundary restriction

p

Gep(p) distribution controls the size of stationary bootstrap. The mean block length is 1/p

lambda

two selections possible for optimal parameter of lambda. "bic" finds lambda from bic criteria, or user can directly input the penalty value

nboot

the number of bootstrap sample for p-value. Default is 100.

n.cl

number of cores in parallel computing. The default is (machine cores - 1)

bound

bound of bic search in "bic" rule. Default is (.001, 1)

gridTF

minimum bic is found by grid search. Default is FALSE

plotTF

Draw plot to see test statistic

Value

A list with component

br The estimated breakpoints including boundary (0, T)

brhist The sequence of breakpoints found from binary splitting

diffhist The history of BIC reduction on each step

W The estimated vectorized autocovariance on each regime.

WI The estimated vectorized precision matrix on each regime.

lambda The penalty parameter estimated on each regime.

pvalhist The empirical p-values on each binary splitting.

fitzero Detailed output at first stage. Useful in producing plot.

Examples

out1= detectGlasso(changesim, p=.2, n.cl=1)