# Visualize EBDBN network A function to visualize graph estimated using the Empirical Bayes Dynamic Bayesian Network (EBDBN) algorithm. ```r ## S3 method for class 'ebdbNet' plot(x, sig.level, interactive = FALSE, clarify = "TRUE", layout = layout.fruchterman.reingold, ...) ``` ## 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 - `clarify`: If TRUE, unconnected nodes should be removed from the plot - `layout`: Layout parameter for graphing network using igraph0 - `...`: 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. ## Author(s) Andrea Rau ## See Also `ebdbn` ## Examples ```r 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) ```

plot.ebdbNet function