Create a graph from verbal fluency data by adding edges for words that occur
adjacent to each other more frequently than
threshold_graph(dat, min_cooc = 2L)
list of character vectors containing the fluency productions.
integer specifying the minimum number of times two words are required to coocur one step apart from each other for an edge to connect those words.
Wulff, D. U., Hills, T., & Mata, R. (2018, October 29). Structural differences in the semantic networks of younger and older adults. https://doi.org/10.31234/osf.io/s73dp
Zemla, J. C., & Austerweil, J. L. (2018). Estimating semantic networks of groups and individuals from fluency data. Computational Brain & Behavior, 1-23.
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# get animal fluency data data(animal_fluency) # infer influence network inferred_network = threshold_graph(animal_fluency) # Simulate ----- # generate watts strogatz graph network = grow_ws(n = 200, k = 10, p = .5) # generate fluency data # sets string equal TRUE as community_graph expects mode character fluency_data = fluency(get_adjlist(network), rep(10, 100), string = TRUE) # infer fluency network inferred_network = threshold_graph(fluency_data)
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