plot_network: Visualize a (partial correlation) network graph of words.

Description

Visualize a (partial correlation) network graph of words.

Usage

plot_network(
  data,
  words = NULL,
  pattern = NULL,
  index = c("pcor", "cor", "glasso", "sim"),
  alpha = 0.05,
  bonf = FALSE,
  max = NULL,
  node.size = "auto",
  node.group = NULL,
  node.color = NULL,
  label.text = NULL,
  label.size = 1.2,
  label.size.equal = TRUE,
  label.color = "black",
  edge.color = c("#009900", "#BF0000"),
  edge.label = FALSE,
  edge.label.size = 1,
  edge.label.color = NULL,
  edge.label.bg = "white",
  file = NULL,
  width = 10,
  height = 6,
  dpi = 500,
  ...
)

Value

Invisibly return a qgraph object, which further can be plotted using plot().

Arguments

data: A wordvec (data.table) or embed (matrix), see data_wordvec_load.
words: [Option 1] Character string(s).
pattern: [Option 2] Regular expression (see str_subset). If neither words nor pattern are specified (i.e., both are NULL), then all words in the data will be extracted.
index: Use which index to perform network analysis? Can be "pcor" (partial correlation, default and suggested), "cor" (raw correlation), "glasso" (graphical lasso-estimation of partial correlation matrix using the glasso package), or "sim" (pairwise cosine similarity).
alpha: Significance level to be used for not showing edges. Defaults to 0.05.
bonf: Bonferroni correction of p value. Defaults to FALSE.
max: Maximum value for scaling edge widths and colors. Defaults to the highest value of the index. Can be 1 if you want to compare several graphs.
node.size: Node size. Defaults to 8*exp(-nNodes/80)+1.
node.group: Node group(s). Can be a named list (see examples) in which each element is a vector of integers identifying the numbers of the nodes that belong together, or a factor.
node.color: Node color(s). Can be a character vector of colors corresponding to node.group. Defaults to white (if node.group is not specified) or the palette of ggplot2 (if node.group is specified).
label.text: Node label of text. Defaults to original words.
label.size: Node label font size. Defaults to 1.2.
label.size.equal: Make the font size of all labels equal. Defaults to TRUE.
label.color: Node label color. Defaults to "black".
edge.color: Edge colors for positive and negative values, respectively. Defaults to c("#009900", "#BF0000").
edge.label: Edge label of values. Defaults to FALSE.
edge.label.size: Edge label font size. Defaults to 1.
edge.label.color: Edge label color. Defaults to edge.color.
edge.label.bg: Edge label background color. Defaults to "white".
file: File name to be saved, should be png or pdf.
width, height: Width and height (in inches) for the saved file. Defaults to 10 and 6.
dpi: Dots per inch. Defaults to 500 (i.e., file resolution: 4000 * 3000).
...: Other parameters passed to qgraph.

Download

Download pre-trained word vectors data (.RData): https://psychbruce.github.io/WordVector_RData.pdf

Examples

Run this code

d = as_embed(demodata, normalize=TRUE)

words = cc("
man, woman,
he, she,
boy, girl,
father, mother,
mom, dad,
China, Japan
")

plot_network(d, words)

p = plot_network(
  d, words,
  node.group=list(Gender=1:6, Family=7:10, Country=11:12),
  node.color=c("antiquewhite", "lightsalmon", "lightblue"),
  file="network.png")
plot(p)

unlink("network.png")  # delete file for code check

# network analysis with centrality plot (see `qgraph` package)
qgraph::centralityPlot(p, include="all", scale="raw",
                       orderBy="Strength")

# graphical lasso-estimation of partial correlation matrix
plot_network(
  d, words,
  index="glasso",
  # threshold=TRUE,
  node.group=list(Gender=1:6, Family=7:10, Country=11:12),
  node.color=c("antiquewhite", "lightsalmon", "lightblue"))

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