multivar_sim: Simulate multivar data.

In multivar: Penalized Estimation of Multiple-Subject Vector Autoregressive (multi-VAR) Models

Simulate multivar data.

Description

Simulate multivar data.

Usage

multivar_sim(
  k,
  d,
  n,
  prop_fill_com,
  prop_fill_ind,
  lb,
  ub,
  sigma,
  unique_overlap = FALSE,
  mat_common = NULL,
  mat_unique = NULL,
  mat_total = NULL,
  diag = FALSE
)

Arguments

k	Integer. The number of individuals (or datasets) to be generated.
d	Integer. The number of variables per dataset. For now this will be constant across individuals.
n	Integer. The time series length.
prop_fill_com	Numeric. The proportion of nonzero paths in the common transition matrix.
prop_fill_ind	Numeric. The proportion of nonzero unique (not in the common transition matrix or transition matrix of other individuals) paths in each individual transition matrix.
lb	Numeric. The upper bound for individual elements of the transition matrices.
ub	Numeric. The lower bound for individual elements of the transition matrices.
sigma	Matrix. The (population) innovation covariance matrix.
unique_overlap	Logical. Default is FALSE. Whether the unique portion should be completely unique (no overlap) or randomly chosen.
mat_common	Matrix. A common effects transition matrix (if known).
mat_unique	List. A list of unique effects transition matrix (if known).
mat_total	List. A list of total effects transition matrix (if known).
diag	Logical. Default is FALSE. Should diagonal elements be filled first for common elements.

Examples

k <- 3
d <- 10
n <- 20
prop_fill_com <- .1
prop_fill_ind <- .05
lb <- 0.1
ub <- 0.5
sigma <- diag(d)
data <- multivar_sim(k, d, n, prop_fill_com, prop_fill_ind, lb, ub,sigma)$data

multivar documentation built on May 28, 2022, 1:08 a.m.