crossClusterMap: Cross-Cluster Mapping Between Concept Maps

In cmAnalysis: Process and Visualise Concept Mapping Data

Cross-Cluster Mapping Between Concept Maps

Description

This function compares two concept maps by aligning their clustering results and visualizing the correspondence between clusters. It identifies matches between clusters from the two maps and highlights differences visually.

Usage

crossClusterMap(conceptMap1, conceptMap2)

Arguments

conceptMap1	An object of class "conceptMap" representing the first concept map.
conceptMap2	An object of class "conceptMap" representing the second concept map.

Details

The function aligns clusters between two concept maps using an optimal matching algorithm. It first creates a matching matrix based on the overlap between clusters in the two maps. Then, it uses the Hungarian algorithm (via the solve_LSAP function from the clue package) to find an optimal alignment of clusters.

The output is a plot that shows the alignment of clusters from the two concept maps, with connecting lines colored to indicate matches or mismatches. Statements not clustered in both maps are highlighted in grey.

Value

The function does not return a value but generates a ggplot2 visualization.

See Also

solve_LSAP, ggplot

Examples

# Simulate data with custom parameters:
set.seed(1)
myCMData <- simulateCardData(nSorters=40, pCorrect=.90, attributeWeights=c(1,1,1,1))

# Subject the data to sorter cluster analysis
myCMDataBySorters <- sorterMapping(myCMData)

# Subject sorter cluster 1 to concept mapping using default "network" method
myCMAnalysis1 <- conceptMapping(myCMDataBySorters[[1]])

# Subject sorter cluster 3 to concept mapping using default "network" method
myCMAnalysis3 <- conceptMapping(myCMDataBySorters[[3]])

# Visualise comparison of results of two sorter clusters
crossClusterMap(myCMAnalysis1, myCMAnalysis3)

cmAnalysis documentation built on April 4, 2025, 4:27 a.m.