sorterMapping {cmAnalysis} | R Documentation |
Cluster Sorters in Concept Mapping Data
Description
This function performs clustering of sorters in concept mapping data based on their sorting behavior. It uses hierarchical clustering and allows the automatic determination of the optimal number of clusters or a user-defined number.
Usage
sorterMapping(
CMData,
numberOfSorterClusters = "auto",
verbose = TRUE,
rangeNumberOfClusters = 2:15,
graph = TRUE
)
Arguments
CMData |
A data frame containing concept mapping data. It must include the columns:
|
numberOfSorterClusters |
Either a character string ( |
verbose |
Logical, if |
rangeNumberOfClusters |
A vector of integers specifying the range of clusters to evaluate
when |
graph |
Logical. If |
Details
This function clusters sorters based on their sorting behavior using hierarchical clustering
with Ward's method. If numberOfSorterClusters = "auto"
, the silhouette method is used
to determine the optimal number of clusters within the range specified by rangeNumberOfClusters
.
Each cluster's data is validated for its suitability for concept mapping, and cluster-specific data
is returned as a list of data frames. Graphical output includes a dendrogram and silhouette plot
if graph = TRUE
.
Value
A list of data frames, each representing the concept mapping data for a cluster of sorters.
If only one cluster is found, the original CMData
is returned.
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 the data to sorter cluster analysis with a predefined number of sorter clusters
myCMDataBySorters <- sorterMapping(myCMData, numberOfSorterClusters=2)