Description Usage Arguments Value References Examples
Create a random walk graph from verbal fluency data that includes edges for words that occur within a window size of 1.
1 | rw_graph(dat)
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dat |
list of character vectors containing the fluency productions. |
A matrix
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | # get animal fluency data
data(animal_fluency)
# infer influence network
inferred_network = rw_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 = rw_graph(fluency_data)
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