overlap_network: Plots the overlap of multiple conditions as bipartite network

In NetFACS: Network Applications to Facial Communication Data

Plots the overlap of multiple conditions as bipartite network

Description

The function takes multiple netfacs objects and plots how different elements connect the conditions, based on the conditional probabilities that the element occurs in the condition and that the condition is seen when the element is present

Usage

overlap_network(
  x,
  min.prob = 0,
  min.count = 5,
  significance = 0.01,
  specificity = 0.1,
  ignore.element = NULL,
  clusters = FALSE,
  plot.bubbles = TRUE
)

Arguments

x	list of objects resulting from specificity or netfacs
min.prob	minimum conditional probability that should be shown in the graph
min.count	minimum number of times that a combination should occur before being included in the graph
significance	sets the level of significance that combinations have to pass before added to the network
specificity	for the 'reduced' graph, select only elements that surpass this context specificity value
ignore.element	string vector, can be used to exclude certain elements when creating the plots
clusters	boolean; if TRUE, the cluster_fast_greedy algorithm is used to detect underlying community structure, based on the occurrence probability network
plot.bubbles	if TRUE, then the nodes in the network plots will be surrounded by bubbles; if FALSE, the edges connect the names directly

Value

Function returns a ggraph plot where each condition is connected to those elements that occur significantly in this condition, and each element is connected to each condition under which it occurs significantly more than expected. Creates four graphs: context specificity, occurrence in that context, a combined graph, and a 'reduced' graph where edges are only included if they pass the 'specificity' value set by the user

Examples

data(emotions_set)
emo.faces <- netfacs_multiple(
  data = emotions_set[[1]],
  condition = emotions_set[[2]]$emotion,
  ran.trials = 10,
  combination.size = 2
)
# calculate element specificity
spec <- specificity(emo.faces)

overlap <- overlap_network(spec,
                           min.prob = 0.01,
                           min.count = 3,
                           significance = 0.01,
                           specificity = 0.5,
                           ignore.element = "25",
                           clusters = TRUE,
                           plot.bubbles = TRUE)

NetFACS documentation built on Dec. 7, 2022, 1:12 a.m.