gen.sim: Generate ordinal multivariate time series data

In tsnetwork: Constructing Dynamic Networks for Time-Series Data

Description

Generating data from time series chain graphical model with vector autoregressive model of order 1.

Usage

gen.sim(t = NULL, n = NULL, p = NULL, k = NULL, network = NULL, prob0 = NULL)

Arguments

t	The number of time points.
n	The number of sample size (observations). The default value is 200.
p	The number of variables. The default value is 90.
k	The number of categories in the data. The default value is 5.
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.
prob0	Initial sparsity level.

Value

An object with S3 class "gen.sim" is returned:

data	A simulated time-series data.
true_theta	Sparse precision matrix.
true_gamma	Sparse autoregressive coefficients matrix.
true_sigma	Covariance matrix.

Author(s)

Pariya Behrouzi and Fentaw Abegaz
Maintainer: Pariya Behrouzi <[e-mail]>

References

1. Behrouzi, P., and Wit, E. C. (2017c). netgwas: An R Package for Network-Based Genome-Wide Association Studies. arXiv preprint, arXiv:1710.01236.

See Also

tsnetwork

Examples

dat <- gen.sim(t = 2, n = 10, p = 5, k = 2, network = "hub")
sim <- dat$data1
prec.true <- dat$theta
autoR.true<- dat$gamma

tsnetwork documentation built on March 26, 2020, 6:45 p.m.