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Visualize a (partial correlation) network graph of words.

Source: R/01-basic.R
plot_network.Rd

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,
  ...
)

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.

Value

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

Download

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

See also

plot_similarity

plot_wordvec_tSNE

Examples

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")
#> ✔ Saved to /home/runner/work/PsychWordVec/PsychWordVec/docs/reference/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"))
#> Warning: A dense regularized network was selected (lambda < 0.1 * lambda.max). Recent work indicates a possible drop in specificity. Interpret the presence of the smallest edges with care. Setting threshold = TRUE will enforce higher specificity, at the cost of sensitivity.