mldfm_subsampling: Subsampling Procedure for MLDFM Estimation

In FARS: Factor-Augmented Regression Scenarios

Subsampling Procedure for MLDFM Estimation

Description

Repeatedly applies the MLDFM estimation to randomly drawn subsamples of the input data.

Usage

mldfm_subsampling(
  data,
  blocks = 1,
  block_ind = NULL,
  global = 1,
  local = NULL,
  middle_layer = NULL,
  method = 0,
  tol = 1e-06,
  max_iter = 1000,
  n_samples = 10,
  sample_size = 0.9,
  seed = NULL
)

Arguments

data	A numeric matrix or data frame containing the time series data. Rows represent time points; columns represent observed variables.
blocks	Integer. The number of blocks into which the data is divided.
block_ind	A vector of integers indicating the end index of each block. Must be of length blocks and in increasing order. Required if blocks > 1.
global	Integer. Number of global factors extracted from the entire dataset.
local	Integer vector of length blocks. Specifies the number of local factors for each block.
middle_layer	Named list. Each name is a string specifying a group of blocks (e.g., "1-3" or "2-3"), and each value is the number of factors to extract.
method	Integer. The method used to initialize the factors: 0 for Canonical Correlation Analysis (CCA), 1 for Principal Component Analysis (PCA).
tol	Numeric. The tolerance level for the residual sum of squares (RSS) minimization process. Used as a convergence criterion.
max_iter	Integer. The maximum number of iterations allowed for the RSS minimization process.
n_samples	Number of subsamples to generate.
sample_size	Proportion of the original sample to retain (e.g., 0.9 for 90%).
seed	Optional integer. Seed for reproducibility of the subsampling process. If NULL, random draws will differ each run.

Value

A list of mldfm objects, one for each subsample.

Examples

data <- matrix(rnorm(1000), nrow = 100, ncol = 100)
block_ind <- c(50,100)  # Defines 3 blocks
local <- c(1, 1)   
result <- mldfm_subsampling(data, blocks = 2, block_ind = block_ind, global = 1, 
local = local, n_samples = 100, sample_size = 0.9)

FARS documentation built on Aug. 8, 2025, 7:33 p.m.