R/data.R

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

#' Simulated multi-VAR data.
#'
#' This dataset contains multivariate time series data for k = 9 individuals with 
#' $d = 10$ variables collected at $t = 100$ equidistant time points. The data was 
#' generated such that each individual's VAR(1) transition matrix has 20 percent 
#' nonzero entries. This means, for example, each individual has 20 nonzero directed 
#' relationships in their data generating model. The position of non-zero elements 
#' in each individual's transition matrix was selected randomly given the following 
#' constraints: 2/3 of  each individual's paths are shared by all individuals, 
#' and 1/3 are unique to each individual. For each individual, coefficient values 
#' between U(0,1, 0.9) were randomly drawn until stability conditions for the 
#' VAR model were satisfied. 
#'
#' @format A list containing
#' \describe{
#'   \item{mat_com}{a common effects transition matrix}
#'   \item{mat_ind_unique}{a list of unique (individual-specific) effect matrices}
#'   \item{mat_ind_final}{a list of total (common + individual-specific) effect matrices}
#'   \item{data}{a list of multivariate time series for all subjects}  
#'   ...
#' }
"dat_multivar_sim"