Create a graph from verbal fluency data by adding edges for words that occur
within a window size
l and retaining those that occur more frequently
min_cooc and the expectations number of chance productions co-
occurences based on
community_graph(dat, l = 3L, min_cooc = 2L, crit = 0.05)
list of character vectors containing the fluency productions.
an integer specifying the window size. The internal upper limit
integer specifying the minimum number of times two words
have to coocur within a window size of
a numeric within
Goni, J., Arrondo, G., Sepulcre, J., Martincorena, I., de Mendizábal, N. V., Corominas-Murtra, B., ... & Villoslada, P. (2011). The semantic organization of the animal category: evidence from semantic verbal fluency and network theory. Cognitive processing, 12(2), 183-196.
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
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# get animal fluency data data(animal_fluency) # infer influence network inferred_network = community_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 = community_graph(fluency_data)
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