ebdbNet (version 1.2.1)

plot.ebdbNet: Visualize EBDBN network

Description

A function to visualize graph estimated using the Empirical Bayes Dynamic Bayesian Network (EBDBN) algorithm.

Usage

## S3 method for class 'ebdbNet':
plot(x, sig.level, interactive = FALSE, ...)

Arguments

x

An object of class "ebdbNet"

sig.level

Desired significance level (between 0 and 1) for edges in network

interactive

If TRUE, interactive plotting through tkplot

...

Additional arguments (mainly useful for plotting)

Details

For input networks, the default colors for nodes representing inputs and genes are green and blue, respectively. For feedback networks, the default color for all nodes is blue. The interactive plotting option should only be used for relatively small networks (less than about 100 nodes).

References

Andrea Rau, Florence Jaffrezic, Jean-Louis Foulley, and R. W. Doerge (2010). An Empirical Bayesian Method for Estimating Biological Networks from Temporal Microarray Data. Statistical Applications in Genetics and Molecular Biology 9. Article 9.

See Also

ebdbn

Examples

library(ebdbNet)
tmp <- runif(1) ## Initialize random number generator
set.seed(125214) ## Save seed

## Simulate data
R <- 5
T <- 10
P <- 10
simData <- simulateVAR(R, T, P, v = rep(10, P), perc = 0.10)
Dtrue <- simData$Dtrue
y <- simData$y

## Simulate 8 inputs
u <- vector("list", R)
M <- 8
for(r in 1:R) {
	u[[r]] <- matrix(rnorm(M*T), nrow = M, ncol = T)
}

####################################################
## Run EB-DBN without hidden states
####################################################
## Choose alternative value of K using hankel if hidden states are to be estimated
## K <- hankel(y)$dim

## Run algorithm	
## net <- ebdbn(y = y, K = 0, input = u, conv.1 = 0.15, conv.2 = 0.10, conv.3 = 0.10,
##	verbose = TRUE)

## Visualize results
## plot(net, sig.level = 0.95)