BGGM (version 2.1.5)

plot.select: Network Plot for select Objects

Description

Visualize the conditional (in)dependence structure.

Usage

# S3 method for select
plot(
  x,
  layout = "circle",
  pos_col = "#009E73",
  neg_col = "#D55E00",
  node_size = 10,
  edge_magnify = 1,
  groups = NULL,
  palette = "Set3",
  ...
)

Value

An object (or list of objects) of class ggplot

that can then be further customized.

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

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)


# }