prune.mca: Remove unnecessary variables from an MCA

In Rsoc/soc.ca: Specific Correspondence Analysis for the Social Sciences

Remove unnecessary variables from an MCA

Description

This function tests and removes variables that have no or too few relations with other variables. In other words variables that only contribute with random noise to the analysis. Removing these variables will tend to increase the strength of the first dimensions and give a wider dispersion of the cloud of cases on the first dimensions. Removing these variables can also give a simpler analysis that is easier to interpret and communicate. The core of the pruning procedure uses the mca.eigen.check to construct a weighted network of relations between variables. Tie strength is measured by the first eigenvalue of an MCA between the two variables. Ties between variables with a weak relationship are removed and variables with few connections to other variables are discarded. With the default values a analysis without irrelevant variables is unchanged. Note that passive categories are inherited from the original analysis and are not included in the mca.eigen.check. This procedure does not help with variables that are too strongly related.

Usage

prune.mca(
  r,
  eigen.cut.off = 0.55,
  network.pruning = TRUE,
  average.pruning = FALSE,
  min.degree = 1
)

Arguments

r	a result object from soc.mca
eigen.cut.off	the cut.off for the first eigen value from mca.eigen.check
network.pruning	If TRUE variables are pruned on the basis their degree
average.pruning	If TRUE variables with a sum of ties below average are discarded. This
min.degree	the minimum number of ties a variable has to have to remain in the analysis

Value

A list containing:

var	a tibble with the weighted degree of the variables
mca.eigen.check	The results from mca.eigen.check
g	a network graph - see igraph
remaining.var	a character vector with the names of the remaining variables
removed	a character vector with the names of the removed variables
pruned.r	A pruned version of the original soc.mca object

References

Inspired by: Durand, Jean-Luc, and Brigitte Le Roux. 2018. “Linkage Index of Variables and its Relationship with Variance of Eigenvalues in PCA and MCA.” Statistica Applicata 29(2):123–35. doi: 10.26398/ijas.0029-006.

Examples

example(soc.mca)      
pr <- prune.mca(result)  
pr$removed               # This example has no irrelevant variables so nothing is removed

Rsoc/soc.ca documentation built on March 7, 2024, 8:59 p.m.