# Network Plot for `select` Objects Visualize the conditional (in)dependence structure. ```r ## S3 method for class 'select' plot( x, layout = "circle", pos_col = "#009E73", neg_col = "#D55E00", node_size = 10, edge_magnify = 1, groups = NULL, palette = "Set3", ... ) ``` ## Arguments - `x`: An object of class `select`. - `layout`: Character string. Which graph layout (defaults is `circle`) ? See gplot.layout . - `pos_col`: Character string. Color for the positive edges (defaults to `green`). - `neg_col`: Character string. Color for the negative edges (defaults to `green`). - `node_size`: Numeric. The size of the nodes (defaults to `10`). - `edge_magnify`: Numeric. A value that is multiplied by the edge weights. This increases (> 1) or decrease (< 1) the line widths (defaults to 1). - `groups`: A character string of length **p** (the number of nodes in the model). This indicates groups of nodes that should be the same color (e.g., "clusters" or "communities"). - `palette`: A character string sepcifying the palette for the `groups`. (default is `Set3`). See [palette options here](http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/). - `...`: Additional options passed to ggnet2 ## Returns An object (or list of objects) of class `ggplot` that can then be further customized. ## Note A more extensive example of a custom plot is provided [here](https://donaldrwilliams.github.io/BGGM/articles/netplot.html) ## Examples ```r ######################### ### example 1: one ggm ## ######################### # data Y <- bfi[,1:25] # estimate fit <- estimate(Y, iter = 250, progress = FALSE) # "communities" comm <- substring(colnames(Y), 1, 1) # edge set E <- select(fit) # plot edge set plt_E <- plot(E, edge_magnify = 5, palette = "Set1", groups = comm) ############################# ### example 2: ggm compare ## ############################# # compare males vs. females # data Y <- bfi[,1:26] Ym <- subset(Y, gender == 1, select = -gender) Yf <- subset(Y, gender == 2, select = -gender) # estimate fit <- ggm_compare_estimate(Ym, Yf, iter = 250, progress = FALSE) # "communities" comm <- substring(colnames(Ym), 1, 1) # edge set E <- select(fit) # plot edge set plt_E <- plot(E, edge_magnify = 5, palette = "Set1", groups = comm) ```

plot.select function