BTIE {MissCP} | R Documentation |
BTIE
Description
Perform the BTIE algorithm to detect the structural breaks in large scale high-dimensional mean shift models.
Usage
BTIE(
data_y,
lambda.1.cv = NULL,
lambda.2.cv = NULL,
max.iteration = 100,
tol = 10^(-2),
block.size = NULL,
refit = FALSE,
optimal.block = TRUE,
optimal.gamma.val = 1.5,
block.range = NULL
)
Arguments
data_y |
input data matrix (response), with each column representing the time series component |
lambda.1.cv |
tuning parmaeter lambda_1 for fused lasso |
lambda.2.cv |
tuning parmaeter lambda_2 for fused lasso |
max.iteration |
max number of iteration for the fused lasso |
tol |
tolerance for the fused lasso |
block.size |
the block size |
refit |
logical; if TRUE, refit the model, if FALSE, use BIC to find a thresholding value and then output the parameter estimates without refitting. Default is FALSE. |
optimal.block |
logical; if TRUE, grid search to find optimal block size, if FALSE, directly use the default block size. Default is TRUE. |
optimal.gamma.val |
hyperparameter for optimal block size, if optimal.blocks == TRUE. Default is 1.5. |
block.range |
the search domain for optimal block size. |
Value
A list object, which contains the followings
Examples
set.seed(1)
n <- 1000;
p <- 50;
brk <- c(333, 666, n+1)
m <- length(brk)
d <- 5
constant.full <- constant_generation(n, p, d, 50, brk)
e.sigma <- as.matrix(1*diag(p))
data_y <- data_generation(n = n, mu = constant.full, sigma = e.sigma, brk = brk)
data_y <- as.matrix(data_y, ncol = p.y)
data_y_miss <- MCAR(data_y, 0.3)
temp <- BTIE(data_y_miss, optimal.block = FALSE, block.size = 30)
temp$cp.final