corclass-package {corclass} | R Documentation |
Correlational Class Analysis package
Description
This package implements the Correlational Class Analysis methodology described by Boutyline (under review). The correlational class analysis of a survey dataset produces a partition of the population into separate modules. This is done in four steps:
Create a matrix G of absolute row correlations. This is the network adjacency matrix.
Set statistically insignificant correlations to 0 to reduce noise.
Use igraph's leading.eigenvector.community to partition this network into modules.
Return an object describing the resulting class assignments (as well as the separate data frames describing the individual modules.)
CCA substantially improves the accuracy of the Relational Class Analysis (RCA) algorithm proposed by Goldberg (2011). See Boutyline (under review) for details.
Details
The main function is cca
. plot.cca
plots the modules produced by cca
. Sample data can be accessed via data(cca.example).
Author(s)
Written and maintained by Andrei Boutyline, andrei.boutyline@gmail.com.
References
Boutyline, Andrei. 2017. "Improving the Measurement of Shared Cultural Schemas with Correlational Class Analysis: Theory and Method." Sociological Science 4:353-93. https://www.sociologicalscience.com/articles-v4-15-353/
See Also
This package makes heavy use of igraph
.
The CCA algorithm is an improvement of RCA https://cran.r-project.org/package=RCA
Examples
data(cca.example)
res1 <- cca(cca.example)
plot(res1, 1)