plot.select: Network Plot for 'select' Objects

In BGGM: Bayesian Gaussian Graphical Models

Network Plot for select Objects

Description

Visualize the conditional (in)dependence structure.

Usage

## 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.
...	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

#########################
### 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)

BGGM documentation built on Sept. 11, 2024, 5:19 p.m.