View source: R/multiple_netfacs_network.R
multiple_netfacs_network  R Documentation 
Takes the results of the nefacs object for combinations of 2 elements and
turns them into a network object (class igraph
and
tbl_graph
) that can be used for further plotting and analyses
multiple_netfacs_network( netfacs.list, link = "unweighted", significance = 0.01, min.count = 1, min.prob = 0, ignore.element = NULL ) multiple.netfacs.network( netfacs.list, link = "unweighted", significance = 0.01, min.count = 1, min.prob = 0, ignore.element = NULL )
netfacs.list 
list of multiple objects resulting from

link 
determines how nodes/elements are connected. 'unweighted' gives a 1 to significant connections and 0 to all others; 'weighted' gives the difference between observed and expected probability of cooccurrence; 'raw' just uses the observed probability of cooccurrence; 'SRI' uses the simple ratio index/affinity (probability of cooccurrence/ (probabilities of each element and the combination)) 
significance 
numeric value, determining the pvalue below which combinations are considered to be dissimilar enough from the null distribution 
min.count 
numeric value, suggesting how many times a combination should at least occur to be displayed 
min.prob 
numeric value, suggesting the probability at which a combination should at least occur to be displayed 
ignore.element 
vector of elements that will not be considered for the network, e.g. because they are too common or too rare or their interpretation is not relevant here 
Function returns a network object where the nodes are the elements, edges represent their cooccurrence, and the vertex and edge attributes contain all additional information from the netfacs object
data(emotions_set) emo.faces < netfacs_multiple( data = emotions_set[[1]], condition = emotions_set[[2]]$emotion, ran.trials = 10, # only for example combination.size = 2 ) emo.nets < multiple_netfacs_network(emo.faces)
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