crossClusterMap {cmAnalysis} | R Documentation |
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 |
conceptMap2 |
An object of class |
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
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)