ggm.first.step.blocks: Threshold block fused lasso step for gaussian graphical...

In LinearDetect: Change Point Detection in High-Dimensional Linear Regression Models

Description

Perform the block fused lasso with thresholding to detect candidate break points.

Usage

ggm.first.step.blocks(
  data_y,
  data_x,
  lambda1,
  lambda2,
  max.iteration = max.iteration,
  tol = tol,
  blocks,
  cv.index,
  HBIC = FALSE,
  gamma.val = NULL
)

Arguments

data_y	input data matrix Y
data_x	input data matrix X
lambda1	tuning parmaeter lambda_1 for fused lasso
lambda2	tuning parmaeter lambda_2 for fused lasso
max.iteration	max number of iteration for the fused lasso
tol	tolerance for the fused lasso
blocks	the blocks
cv.index	the index of time points for cross-validation
HBIC	logical; if TRUE, use high-dimensional BIC, if FALSE, use orginal BIC. Default is FALSE.
gamma.val	hyperparameter for HBIC, if HBIC == TRUE.

Value

A list object, which contains the followings

jump.l2

estimated jump size in L2 norm

jump.l1

estimated jump size in L1 norm

pts.list

estimated change points in the first step

beta.full

estimated parameters in the first step

LinearDetect documentation built on March 22, 2021, 9:06 a.m.