SparseTSCGM (version 1.0)

SparseTSCGM-package: Sparse time series chain graphical models.

Description

Computes sparse autoregressive coefficient and precision matrices for time series chain graphical models.

References

Fentaw Abegaz and Ernst wit (2013) Sparse time series chain graphical models for reconstructing genetic networks. Biostatistics, doi:10.1093/biostatistics/kxt005. 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

#Simulated data from random graph
set.seed(1)
simu <- sim.data(time=10,n_obs=5, n_var=20,prob0=0.15)
Sdata <- simu$data1

lam1=0.43
lam2=0.27

res.tscgm <- sparse.tscgm(data=Sdata, lam1=lam1, lam2=lam2, B.start = NULL, 
              model.select="null", maxit.out = 100, maxit.in = 500, 
             tol.out = 1e-04, tol.in = 1e-05, silent = TRUE) 
B <- res.tscgm$Bhat
om <- res.tscgm$omega

### Plot chain graph

  ### plot of undirected graph
  
colnames(om) <-  rownames(om) <- colnames(Sdata)
network_precm <- network(om)
plot.network(network_precm,label = network.vertex.names(network_precm),
  usearrows = FALSE, displayisolates = FALSE, pad = 0.01,label.pad = 0.3, 
  label.col = 6, vertex.col = 5,vertex.cex = 1.5,
  edge.col = 4, mode = "fruchtermanreingold", interactive=FALSE)

   ### Plot of directed graph
 
colnames(B) <-  rownames(B) <- colnames(Sdata)
network.autoregm <- network(B)
plot.network(network.autoregm,label = network.vertex.names(network.autoregm),
  usearrows = TRUE, displayisolates = FALSE, pad = 0.01,label.pad = 0.3, 
  label.col = 6, vertex.col = 5,vertex.cex = 1.5,
  edge.col = 3, mode = "fruchtermanreingold", interactive=FALSE)