Memberships from community detection algorithms do not always
align numerically. This function seeks to homogenize
community memberships between a target membership (the
membership to homogenize toward) and one or more other
memberships. This function is the core of the
dimensionStability and
itemStability functions
Arguments
- target.membership
Vector, matrix, or data frame. The target memberships that all other memberships input into
convert.membershipshould be homogenize toward- convert.membership
Vector, matrix, or data frame. Either a vector of memberships the same length as
target.membershipor a matrix or data frame of many membership solutions with either across rows or down columns the same length astarget.membership(this function will automatically determine this orientation for you with precedence given solutions across rows)
Value
Returns a vector or matrix the length or size of
convert.membership with memberships homogenized toward
target.membership
References
Original implementation of bootEGA
Christensen, A. P., & Golino, H. (2021).
Estimating the stability of the number of factors via Bootstrap Exploratory Graph Analysis: A tutorial.
Psych, 3(3), 479-500.
Author
Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>
Examples
# Get network
network <- network.estimation(wmt2[,7:24])
# Apply Walktrap
network_walktrap <- community.detection(
network, algorithm = "walktrap"
)
# Apply Louvain
network_louvain <- community.detection(
network, algorithm = "louvain"
)
# Homogenize toward Walktrap
community.homogenize(network_walktrap, network_louvain)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] 1 1 1 1 1 2 2 2 3 3 2 2 3 3
#> [,15] [,16] [,17] [,18]
#> [1,] 2 3 2 2