Sparse Time Series Chain Graphical Models.

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Description

Computes sparse autoregressive coefficient and precision matrices for time series chain graphical models(TSCGM). These models provide an effeicient way of simultaneously dealing with Gaussian graphical models (undirected graphs for instantaneous interactions) and Bayesian networks (directed graphs for dynamic interactions) for reconstructing instantaneous and dynamic networks from repeated multivariate time series data.

Details

Package: SparseTSCGM
Type: Package
Version: 2.5
Date: 2015-12-11
License: GPL (>=3)
LazyLoad: yes

Author(s)

Fentaw Abegaz and Ernst Wit

Maintainer: Fentaw Abegaz <f.abegaz.yazew@rug.nl>

References

Fentaw Abegaz and Ernst Wit (2013). Sparse time series chain graphical models for reconstructing genetic networks. Biostatistics. 14, 3: 586-599.

Rothman, A.J., Levina, E., and Zhu, J. (2010). Sparse multivariate regression with covariance estimation. Journal of Computational and Graphical Statistics. 19: 947–962.

Examples

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seed = 321
datas <- sim.data(model="ar1", time=10,n.obs=10, n.var=5,seed=seed,prob0=0.35,
         network="random")
data.fit <-  datas$data1
prec_true <- datas$theta
autoR_true <- datas$gamma
    
   
res.tscgm <- sparse.tscgm(data=data.fit, lam1=NULL, lam2=NULL, nlambda=NULL, 
 model="ar1", penalty="scad", optimality="bic_mod",
 control=list(maxit.out = 10, maxit.in = 100))
   
#Estimated sparse precision and autoregression matrices
prec <- res.tscgm$theta
autoR <- res.tscgm$gamma

#Graphical visualization
par(mfrow=c(2,2))
plot.tscgm(datas, mat="precision",main="True precision matrix")         
plot.tscgm(res.tscgm, mat="precision",main="Estimated precision matrix")     
plot.tscgm(datas, mat="autoregression",main="True autoregression coef. matrix")    
plot.tscgm(res.tscgm, mat="autoregression",
           main="Estimated autoregression coef. matrix")