corclass-package: Correlational Class Analysis package

In corclass: Correlational Class Analysis

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:

1. Create a matrix G of absolute row correlations. This is the network adjacency matrix.

2. Set statistically insignificant correlations to 0 to reduce noise.

3. Use igraph's leading.eigenvector.community to partition this network into modules.

4. 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, [e-mail].

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) 

corclass documentation built on Sept. 8, 2023, 5:50 p.m.