memnet (version 0.1.0)

ffluency: Fast verbal fluency generator

Description

Generates multiple verbal fluency sequences using one_ffluency.

Usage

ffluency(adjlist, n, pjump = 0, type = 0L, string = FALSE)

Arguments

adjlist

a list containing row indices of nodes adjacent node to the ith node as created by get_adjlist.

n

integer vector specifying for each sequence the maximum numbers of productions. Function may return fewer than n.

pjump

numeric specifying the probability of a jump.

type

integer controlling network start and jump nodes. For type = 0 the process selects the start node and any jump nodes proportional to their degree. For type = 1 the process selects a random node to serve both as the start node and the jump node. For type = 2 the process selects the start and any jump nodes uniformly at random.

string

logical specifying whether the output should be of mode character.

Value

List of character vectors containing the indices of the fluency productions. Indices refer to the row of the item in the original adjacency matrix. See get_adjlist.

Details

For details see one_ffluency.

References

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

Goni, J., Martincorena, I., Corominas-Murtra, B., Arrondo, G., Ardanza- Trevijano, S., & Villoslada, P. (2010). Switcher-random-walks: A cognitive- inspired mechanism for network exploration. International Journal of Bifurcation and Chaos, 20(03), 913-922.

Examples

# NOT RUN {
# generate watts strogatz graph
network = grow_ws(n = 100, k = 10)

# create verbal fluency sequences
ffluency(get_adjlist(network), c(10, 10))

# create verbal fluency sequence
# with high jump probability
ffluency(get_adjlist(network), c(10, 10), pjump = .5)

# }