Network Plot for select Objects

Source: R/plot.select.R

Visualize the conditional (in)dependence structure.

# S3 method for 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.
...

Additional options passed to ggnet2

Value

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

Examples

# \donttest{
#########################
### 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)


# }