lstsp: Main function for the low rank plus sparse structure VAR...

In VARDetect: Multiple Change Point Detection in Structural VAR Models

Main function for the low rank plus sparse structure VAR model

Description

Main function for the low-rank plus sparse structure VAR model

Usage

lstsp(
  data,
  lambda.1 = NULL,
  mu.1 = NULL,
  lambda.1.seq = NULL,
  mu.1.seq = NULL,
  lambda.2,
  mu.2,
  lambda.3,
  mu.3,
  alpha_L = 0.25,
  omega = NULL,
  h = NULL,
  step.size = NULL,
  tol = 1e-04,
  niter = 100,
  backtracking = TRUE,
  skip = 5,
  cv = FALSE,
  nfold = NULL,
  verbose = FALSE
)

Arguments

data	A n by p dataset matrix
lambda.1	tuning parameter for sparse component for the first step
mu.1	tuning parameter for low rank component for the first step
lambda.1.seq	a sequence of lambda to the left segment for cross-validation, it's not mandatory to provide
mu.1.seq	a sequence of mu to the left segment, low rank component tuning parameter
lambda.2	tuning parameter for sparse for the second step
mu.2	tuning parameter for low rank for the second step
lambda.3	tuning parameter for estimating sparse components
mu.3	tuning parameter for estimating low rank components
alpha_L	a positive numeric value, indicating the restricted space of low rank component, default is 0.25
omega	tuning parameter for information criterion, the larger of omega, the fewer final selected change points
h	window size of the first rolling window step
step.size	rolling step
tol	tolerance for the convergence in the second screening step, indicates when to stop
niter	the number of iterations required for FISTA algorithm
backtracking	A boolean argument to indicate use backtrack to FISTA model
skip	The number of observations need to skip near the boundaries
cv	A boolean argument, indicates whether the user will apply cross validation to select tuning parameter, default is FALSE
nfold	An positive integer, the number of folds for cross validation
verbose	If is TRUE, then it will print all information about current step.

Value

A list object including

data

the original dataset

q

the time lag for the time series, in this case, it is 1

cp

Final estimated change points

sparse_mats

Final estimated sparse components

lowrank_mats

Final estimated low rank components

est_phi

Final estimated model parameter, equals to sum of low rank and sparse components

time

Running time for the LSTSP algorithm

Examples

nob <- 100
p <- 15
brk <- c(50, nob+1)
rank <- c(1, 3)
signals <- c(-0.7, 0.8)
singular_vals <- c(1, 0.75, 0.5)
info_ratio <- rep(0.35, 2)
try <- simu_var(method = "LS", nob = nob, k = p, lags = 1, brk = brk, 
                sigma = as.matrix(diag(p)), signals = signals, 
                rank = rank, singular_vals = singular_vals, info_ratio = info_ratio, 
                sp_pattern = "off-diagonal", spectral_radius = 0.9)
data <- try$series

lambda1 = lambda2 = lambda3 <- c(2.5, 2.5)
mu1 = mu2 = mu3 <- c(15, 15)
fit <- lstsp(data, lambda.1 = lambda1, mu.1 = mu1, 
             lambda.2 = lambda2, mu.2 = mu2, 
             lambda.3 = lambda3, mu.3 = mu3, alpha_L = 0.25, 
             step.size = 5, niter = 20, skip = 5, 
             cv = FALSE, verbose = FALSE)
summary(fit)
plot(fit, data, display = "cp")
plot(fit, data, display = "param")

VARDetect documentation built on May 10, 2022, 9:07 a.m.