TY - EJOU
AU - Ryoo, Ji Hoon
AU - Park, Seohee
AU - Kim, Seongeun
AU - Hwang, Heungsun
TI - gscaLCA in R: Fitting Fuzzy Clustering Analysis Incorporated with Generalized Structured Component Analysis
T2 - Computer Modeling in Engineering \& Sciences
PY - 2022
VL - 132
IS - 3
SN - 1526-1506
AB - Clustering analysis identifying unknown heterogenous subgroups of a population (or a sample) has become
increasingly popular along with the popularity of machine learning techniques. Although there are many software
packages running clustering analysis, there is a lack of packages conducting clustering analysis within a structural
equation modeling framework. The package, gscaLCA which is implemented in the R statistical computing
environment, was developed for conducting clustering analysis and has been extended to a latent variable modeling.
More specifically, by applying both fuzzy clustering (FC) algorithm and generalized structured component analysis
(GSCA), the package gscaLCA computes membership prevalence and item response probabilities as posterior
probabilities, which is applicable in mixture modeling such as latent class analysis in statistics. As a hybrid
model between data clustering in classifications and model-based mixture modeling approach, fuzzy clusterwise
GSCA, denoted as gscaLCA, encompasses many advantages from both methods: (1) soft partitioning from FC
and (2) efficiency in estimating model parameters with bootstrap method via resolution of global optimization
problem from GSCA. The main function, gscaLCA, works for both binary and ordered categorical variables. In
addition, gscaLCA can be used for latent class regression as well. Visualization of profiles of latent classes based
on the posterior probabilities is also available in the package gscaLCA. This paper contributes to providing a
methodological tool, gscaLCA that applied researchers such as social scientists and medical researchers can apply
clustering analysis in their research.
KW - Fuzzy clustering; generalized structured component analysis; gscaLCA; latent class analysis
DO - 10.32604/cmes.2022.019708