sim.data {SparseTSCGM} | R Documentation |
Generates sparse vector autoregressive coefficients matrices and precision matrix from various network structures and using these matrices generates repeated multivariate time series dataset.
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)
model |
Specifies the order of vector autoregressive models. Vector autoregressive
model of order 1 is applied if |
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 |
prec |
Precision matrix. |
gamma1 |
Autoregressive coefficients matrix at time lag 1. |
gamma2 |
Autoregressive coefficients matrix at time lag 2. |
A list containing:
theta |
Sparse precision matrix. |
gamma |
Sparse autoregressive coefficients matrix. |
sigma |
Covariance matrix. |
data1 |
Repeated multivariate time series data in longitudinal format. |
Fentaw Abegaz and Ernst Wit
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