sim.data: Multivariate time series simulation with chain graphical...

In SparseTSCGM: Sparse Time Series Chain Graphical Models

Description

Generates sparse vector autoregressive coefficients matrices and precision matrix from various network structures and using these matrices generates repeated multivariate time series dataset.

Usage

 sim.data(model=c("ar1","ar2"),time=time,n.obs=n.obs, n.var=n.var,seed=NULL,
          prob0=NULL, network=c("random","scale-free","hub","user_defined"),
          prec=NULL,gamma1=NULL,gamma2=NULL)

Arguments

model	Specifies the order of vector autoregressive models. Vector autoregressive model of order 1 is applied if model = "ar1" and Vector autoregressive model of order 2 is applied if method = "ar2".
time	Number of time points.
n.obs	Number of observations or replicates.
n.var	Number of variables.
seed	Random number seed.
prob0	Initial sparsity level.
network	Specifies the type of network structure. This could be random, scale-free, hub or user defined structures. Details on simultions from the various network structures can be found in the R package flare.
prec	Precision matrix.
gamma1	Autoregressive coefficients matrix at time lag 1.
gamma2	Autoregressive coefficients matrix at time lag 2.

Value

A list containing:

theta	Sparse precision matrix.
gamma	Sparse autoregressive coefficients matrix.
sigma	Covariance matrix.
data1	Repeated multivariate time series data in longitudinal format.

Author(s)

Fentaw Abegaz and Ernst Wit

Examples

seed = 321
datas <- sim.data(model="ar1", time=4,n.obs=3, n.var=5,seed=seed,prob0=0.35,
         network="random")
data.ts <-  datas$data1
prec_true <- datas$theta
autoR_true <- datas$gamma

Example output

Generating data from the multivariate normal distribution with the random graph structure...
done.
Generating data from the multivariate normal distribution with the random graph structure...
done.
Generating data from the multivariate normal distribution with the random graph structure...
done.

SparseTSCGM documentation built on Jan. 16, 2021, 5:10 p.m.