community_graph {memnet} | R Documentation |
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
than min_cooc
and the expectations number of chance productions co-
occurences based on 1-crit
.
community_graph(dat, l = 3L, min_cooc = 2L, crit = 0.05)
dat |
list of character vectors containing the fluency productions. |
l |
an integer specifying the window size. The internal upper limit
of |
min_cooc |
integer specifying the minimum number of times two words
have to coocur within a window size of |
crit |
a numeric within |
A matrix
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
# 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)