| Title: | Process and Visualise Concept Mapping Data |
|---|---|
| Description: | Processing and visualizing concept mapping data. Concept maps are versatile tools used across disciplines to enhance understanding, teaching, brainstorming, and information organization. The analysis of concept mapping data involves the sequential use of cluster analysis (for sorting participants and statements), multidimensional scaling (for positioning statements in a conceptual space), and visualization techniques, including point cluster maps and dendrograms. |
| Authors: | Jos Hageman [aut, cre], Jarl Kampen [aut] |
| Maintainer: | Jos Hageman <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.0 |
| Built: | 2025-03-14 04:48:42 UTC |
| Source: | https://github.com/cran/cmAnalysis |
This function processes the "statement" column of a data frame containing concept map data by converting each statement to lowercase, removing stopwords, and truncating the statement to a specified maximum length. It allows for the abbreviation of long statements while maintaining their core meaning by removing unnecessary words.
abbreviateStatements(CMData, max_length = 30)abbreviateStatements(CMData, max_length = 30)
CMData |
A data frame containing concept map data. The data should have at least the following column:
|
max_length |
An integer specifying the maximum number of characters for the abbreviated statement. Default is 30. |
This function performs several preprocessing steps on the "statement" column:
Converts statements to lowercase.
Removes punctuation and stopwords from the statements.
Truncates statements to a specified maximum length.
Removes any rows with empty statements after processing.
Stopwords are predefined and include common English words (e.g., "the", "and", "is", "a", etc.) that do not contribute much meaning to the core idea of the statement.
A data frame with the same structure as the input, but with an updated "statement" column containing the abbreviated statements.
# Create a sample data frame with concept map data conceptMapData <- data.frame( id = c(1, 2, 3), statement = c( "The quick brown fox jumps over the lazy dog", "This is a simple concept map example", "Data science involves analyzing datasets" ) ) # Apply the abbreviateStatements function with a maximum length of 20 result <- abbreviateStatements(conceptMapData, max_length = 20) print(result)# Create a sample data frame with concept map data conceptMapData <- data.frame( id = c(1, 2, 3), statement = c( "The quick brown fox jumps over the lazy dog", "This is a simple concept map example", "Data science involves analyzing datasets" ) ) # Apply the abbreviateStatements function with a maximum length of 20 result <- abbreviateStatements(conceptMapData, max_length = 20) print(result)
Checks whether the provided data frame meets the requirements for concept map data.
checkConceptMapData(CMData)checkConceptMapData(CMData)
CMData |
A data frame containing concept map data. This data frame must include the required columns: "sorterID", "statement", and "stackID". |
This function performs the following checks on the input data:
Verifies that CMData is a data frame.
Ensures the presence of required columns: "sorterID", "statement", and "stackID".
Confirms that there are at least 2 unique values in the "stackID" column.
Confirms that there are at least 2 unique values in the "sorterID" column.
Confirms that there are at least 2 unique values in the "statement" column.
Returns TRUE if all checks pass. If any check fails, an error is raised
with a descriptive message.
# Example of valid data validData <- data.frame( sorterID = c("resp1", "resp1", "resp1", "resp2", "resp2", "resp2", "resp3", "resp3", "resp3"), statement = c("London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin"), stackID = c("capital city", "city", "capital city", 1, 2, 2, "A", "B", "A") ) checkConceptMapData(validData) # Should return TRUE # Example of invalid data (missing columns) invalidData <- data.frame( sorterID = c(1, 2), stackID = c(1, 2) ) # checkConceptMapData(invalidData) # Will return False as an error# Example of valid data validData <- data.frame( sorterID = c("resp1", "resp1", "resp1", "resp2", "resp2", "resp2", "resp3", "resp3", "resp3"), statement = c("London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin"), stackID = c("capital city", "city", "capital city", 1, 2, 2, "A", "B", "A") ) checkConceptMapData(validData) # Should return TRUE # Example of invalid data (missing columns) invalidData <- data.frame( sorterID = c(1, 2), stackID = c(1, 2) ) # checkConceptMapData(invalidData) # Will return False as an error
This function conducts a concept mapping analysis on sorter data, producing a concept map based on one of three clustering methods: k-means, network analysis, or classical multidimensional scaling (CMDS).
conceptMapping( CMData, method = "network", numberOfClusters = "auto", verbose = TRUE, rangeNumberOfClusters = 2:15, graph = FALSE, numberOfKmeansRestarts = 100, backgroundColor = "black", main = NULL, ... )conceptMapping( CMData, method = "network", numberOfClusters = "auto", verbose = TRUE, rangeNumberOfClusters = 2:15, graph = FALSE, numberOfKmeansRestarts = 100, backgroundColor = "black", main = NULL, ... )
CMData |
A data frame containing concept mapping data.
It must include the columns:
|
method |
A string specifying the clustering method to use. Options are:
|
numberOfClusters |
Either a character string ( |
verbose |
Logical, if |
rangeNumberOfClusters |
A vector of integers specifying the range of clusters to evaluate
when |
graph |
Logical. If |
numberOfKmeansRestarts |
An integer specifying the number of restarts for k-means clustering.
Only relevant if |
backgroundColor |
A string specifying the background color of network plots. Default is |
main |
A string specifying the title for plots. Default is |
... |
Additional arguments, such as |
The function supports three methods for clustering:
"kmeans": Uses k-means clustering with an optional silhouette-based determination of cluster count.
"network": Generates a network plot using modularity-based clustering.
"cmds": Applies classical multidimensional scaling (CMDS) and clusters the results.
Heatmaps are created for all methods, while additional visualizations depend on the chosen method and graph parameter.
An object of class conceptMap, containing:
allStatementsA data frame with statement numbers and text.
CMDataThe original concept mapping data.
methodThe clustering method used.
numberOfClustersThe number of clusters identified.
clusterResultsA vector indicating cluster assignments for each statement.
heatmapPlotA heatmap visualizing co-occurrence patterns.
silhouettePlot(If applicable) A silhouette plot for "kmeans" or "cmds".
networkPlot(If applicable) A network plot for "network".
cmdsPlot(If applicable) A CMDS plot for "cmds".
# 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) # Concept mapping on sorter cluster 3 using default "network" method myCMAnalysis3 <- conceptMapping(myCMDataBySorters[[3]]) # Concept mapping using default network method using 3 clusters myCMAnalysis3b <- conceptMapping(myCMDataBySorters[[3]], numberOfCluster = 3) # Concept mapping using kmeans clustering and 3 clusters myCMAnalysis3c <- conceptMapping(myCMDataBySorters[[3]], method = "kmeans", numberOfCluster = 3)# 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) # Concept mapping on sorter cluster 3 using default "network" method myCMAnalysis3 <- conceptMapping(myCMDataBySorters[[3]]) # Concept mapping using default network method using 3 clusters myCMAnalysis3b <- conceptMapping(myCMDataBySorters[[3]], numberOfCluster = 3) # Concept mapping using kmeans clustering and 3 clusters myCMAnalysis3c <- conceptMapping(myCMDataBySorters[[3]], method = "kmeans", numberOfCluster = 3)
This function generates an overview of all unique statements in a given concept mapping dataset. The function assigns a unique statement number to each distinct statement and returns a summary data frame.
createStatementOverview(CMData)createStatementOverview(CMData)
CMData |
A data frame containing concept map data. This must include a column named |
The function first checks if the provided dataset is suitable for concept mapping using the checkConceptMapData function. If the data is valid, it assigns a numeric statement number
to each distinct statement, ordered by their appearance in the dataset. The function then generates
an overview where each statement is paired with its corresponding statement number.
A data frame with two columns: StatementNumber and Statement.
Each row represents a unique statement with its corresponding number.
# Example of valid data CMData <- data.frame( sorterID = c("resp1", "resp1", "resp1", "resp2", "resp2", "resp2", "resp3", "resp3", "resp3"), statement = c("London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin"), stackID = c("capital city", "city", "capital city", 1, 2, 2, "A", "B", "A") ) # Create an overview of the statements createStatementOverview(CMData)# Example of valid data CMData <- data.frame( sorterID = c("resp1", "resp1", "resp1", "resp2", "resp2", "resp2", "resp3", "resp3", "resp3"), statement = c("London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin"), stackID = c("capital city", "city", "capital city", 1, 2, 2, "A", "B", "A") ) # Create an overview of the statements createStatementOverview(CMData)
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.
crossClusterMap(conceptMap1, conceptMap2)crossClusterMap(conceptMap1, conceptMap2)
conceptMap1 |
An object of class |
conceptMap2 |
An object of class |
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.
The function does not return a value but generates a ggplot2 visualization.
# 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)# 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)
This function calculates the number of unique sorters (users) in a given concept mapping dataset.
numberOfSorters(CMData, verbose = TRUE)numberOfSorters(CMData, verbose = TRUE)
CMData |
A data frame containing concept map data. This must include a column named |
verbose |
A logical, if |
The function first checks if the provided dataset is suitable for concept mapping using the checkConceptMapData function. If the data is valid, it calculates and returns the number
of unique sorterIDs.
An integer representing the number of unique sorters in the dataset.
# Example of valid data CMData <- data.frame( sorterID = c("resp1", "resp1", "resp1", "resp2", "resp2", "resp2", "resp3", "resp3", "resp3"), statement = c("London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin"), stackID = c("capital city", "city", "capital city", 1, 2, 2, "A", "B", "A") ) # Count the number of sorters silently numberOfSorters(CMData, verbose = FALSE) # Count the number of sorters with message numberOfSorters(CMData)# Example of valid data CMData <- data.frame( sorterID = c("resp1", "resp1", "resp1", "resp2", "resp2", "resp2", "resp3", "resp3", "resp3"), statement = c("London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin"), stackID = c("capital city", "city", "capital city", 1, 2, 2, "A", "B", "A") ) # Count the number of sorters silently numberOfSorters(CMData, verbose = FALSE) # Count the number of sorters with message numberOfSorters(CMData)
This function calculates the number of unique statements in a given concept mapping dataset.
numberOfStatements(CMData, verbose = TRUE)numberOfStatements(CMData, verbose = TRUE)
CMData |
A data frame containing concept map data. This must include a column named |
verbose |
A logical, if |
The function first checks if the provided dataset is suitable for concept mapping using the checkConceptMapData function. If the data is valid, it calculates and returns the number
of unique statements.
An integer representing the number of unique statements in the dataset.
# Example of valid data CMData <- data.frame( sorterID = c("resp1", "resp1", "resp1", "resp2", "resp2", "resp2", "resp3", "resp3", "resp3"), statement = c("London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin"), stackID = c("capital city", "city", "capital city", 1, 2, 2, "A", "B", "A") ) # Count the number of statements silently numberOfStatements(CMData, verbose = FALSE) # Count the number of statements with message numberOfStatements(CMData)# Example of valid data CMData <- data.frame( sorterID = c("resp1", "resp1", "resp1", "resp2", "resp2", "resp2", "resp3", "resp3", "resp3"), statement = c("London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin"), stackID = c("capital city", "city", "capital city", 1, 2, 2, "A", "B", "A") ) # Count the number of statements silently numberOfStatements(CMData, verbose = FALSE) # Count the number of statements with message numberOfStatements(CMData)
This function generates visualizations for an object of class conceptMap.
Depending on the method used to create the concept map, different types of plots
(e.g., heatmap, silhouette, network, or CMDS plots) are available.
## S3 method for class 'conceptMap' plot(x, whichPlot = "all", ...)## S3 method for class 'conceptMap' plot(x, whichPlot = "all", ...)
x |
An object of class |
whichPlot |
A character string specifying which plot to display. Options depend on the method used to create the concept map:
The default is |
... |
arguments to be passed to methods |
The function behaves differently depending on the method used to create
the concept map. The conceptMap object must include attributes such as
method (e.g., "kmeans", "network", or "cmds") and the corresponding plot
objects (e.g., heatmapPlot, silhouettePlot, etc.).
The following methods are supported:
"kmeans": Supports "heatmap" and "silhouette" plots.
"network": Supports "heatmap" and "network" plots.
"cmds": Supports "heatmap", "silhouette", and "cmds" plots.
This function displays the specified plot(s) in the current graphical device.
# 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 3 to concept mapping using default "network" method myCMAnalysis3 <- conceptMapping(myCMDataBySorters[[3]]) # Visualise the concept map plot(myCMAnalysis3)# 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 3 to concept mapping using default "network" method myCMAnalysis3 <- conceptMapping(myCMDataBySorters[[3]]) # Visualise the concept map plot(myCMAnalysis3)
This function prints a detailed summary of a concept map object, including information about statements, sorters, clusters, and the specific statements within selected clusters.
## S3 method for class 'conceptMap' print(x, whichCluster = NULL, ...)## S3 method for class 'conceptMap' print(x, whichCluster = NULL, ...)
x |
An object of class |
whichCluster |
A vector of cluster numbers to display. If |
... |
arguments to be passed to methods |
The function first checks if the input object is of class "conceptMap" and validates the
requested clusters (if specified). It provides an overview of the number of statements, sorters,
and clusters. For each requested cluster, the function lists the statements included in that cluster.
This function does not return a value; it prints the details of the concept map to the console.
# 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 3 to concept mapping using default "network" method myCMAnalysis3 <- conceptMapping(myCMDataBySorters[[3]]) # Print content of concept map of sorter cluster 3 print(myCMAnalysis3)# 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 3 to concept mapping using default "network" method myCMAnalysis3 <- conceptMapping(myCMDataBySorters[[3]]) # Print content of concept map of sorter cluster 3 print(myCMAnalysis3)
This function retrieves and optionally prints the unique sorters (users) in a given concept mapping dataset.
printSorters(CMData, verbose = TRUE)printSorters(CMData, verbose = TRUE)
CMData |
A data frame containing concept map data. This must include a column named |
verbose |
A logical, if |
The function first checks if the provided dataset is suitable for concept mapping using the checkConceptMapData function. If the data is valid, it retrieves the unique sorter IDs
from the sorterID column. If verbose = TRUE, the function prints the sorter IDs.
A vector of unique sorter IDs.
# Example of valid data CMData <- data.frame( sorterID = c("resp1", "resp1", "resp1", "resp2", "resp2", "resp2", "resp3", "resp3", "resp3"), statement = c("London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin"), stackID = c("capital city", "city", "capital city", 1, 2, 2, "A", "B", "A") ) # Retrieve unique sorters without printing printSorters(CMData, verbose = FALSE) # Retrieve and print unique sorters to console printSorters(CMData)# Example of valid data CMData <- data.frame( sorterID = c("resp1", "resp1", "resp1", "resp2", "resp2", "resp2", "resp3", "resp3", "resp3"), statement = c("London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin", "London", "Frankfurt", "Berlin"), stackID = c("capital city", "city", "capital city", 1, 2, 2, "A", "B", "A") ) # Retrieve unique sorters without printing printSorters(CMData, verbose = FALSE) # Retrieve and print unique sorters to console printSorters(CMData)
This function selects statements from a concept map dataset based on their significance in terms of co-occurrence. It applies a chi-squared test on the co-occurrence matrix of the statements to identify those that are statistically significant (i.e., those that co-occur more frequently than would be expected by chance).
selectStatements(CMData, significanceThreshold = 0.05, verbose = TRUE, ...)selectStatements(CMData, significanceThreshold = 0.05, verbose = TRUE, ...)
CMData |
A data frame containing concept map data. The data should have at least the following columns:
|
significanceThreshold |
A numeric value representing the significance threshold for the chi-squared test. Statements with p-values less than this threshold are considered significant. Default is 0.05. |
verbose |
Logical, if |
... |
Additional arguments to be passed to the chi-squared test (optional). |
A data frame with the same structure as the input, but with non-significant statements removed (if any). If all statements are significant, the original data frame is returned unchanged.
chisq.test for chi-squared test functionality.
# Simulate data with custom parameters: set.seed(1) myCMData <- simulateCardData(nSorters=40, pCorrect=.70, attributeWeights=c(1,1,1,1)) # Subject the data to sorter cluster analysis myCMDataBySorters <- sorterMapping(myCMData) # Select significant statements mySelectedStatementsSorterCluster3 <- selectStatements(myCMDataBySorters[[1]])# Simulate data with custom parameters: set.seed(1) myCMData <- simulateCardData(nSorters=40, pCorrect=.70, attributeWeights=c(1,1,1,1)) # Subject the data to sorter cluster analysis myCMDataBySorters <- sorterMapping(myCMData) # Select significant statements mySelectedStatementsSorterCluster3 <- selectStatements(myCMDataBySorters[[1]])
This function simulates card sorting data based on user-specified parameters, such as the number of sorters, the probability of correct sorting, and the weights for different card attributes.
simulateCardData( nSorters = 40, pCorrect = 0.95, attributeWeights = c(1, 1, 1, 1), verbose = TRUE )simulateCardData( nSorters = 40, pCorrect = 0.95, attributeWeights = c(1, 1, 1, 1), verbose = TRUE )
nSorters |
An integer specifying the number of sorters to simulate. Default is 40. |
pCorrect |
A numeric value between 0 and 1 specifying the probability that a card is sorted correctly. Default is 0.95. |
attributeWeights |
A numeric vector of length 4 specifying the weights
for the card attributes (e.g., |
verbose |
Logical, if |
The function simulates a card sorting experiment where cards are
sorted by multiple sorters based on one of four attributes. The probability
of sorting a card correctly is determined by pCorrect, and errors are
introduced randomly for each sorter. The attribute weights determine how many
sorters focus on each attribute, and a warning is issued if the weights do
not align with the total number of sorters.
The function returns a data frame containing simulated card sorting data for all sorters, including sorter IDs, card IDs, and assigned stacks.
A data frame with columns:
sorterIDA unique identifier for each sorter.
statementThe ID of the card being sorted.
stackIDThe stack assigned to the card by the sorter.
# Simulate data with default parameters: set.seed(1) myCMData <- simulateCardData() # Simulate data with custom parameters: set.seed(1) myCMData <- simulateCardData(nSorters=40, pCorrect=.90, attributeWeights=c(1,1,1,1))# Simulate data with default parameters: set.seed(1) myCMData <- simulateCardData() # Simulate data with custom parameters: set.seed(1) myCMData <- simulateCardData(nSorters=40, pCorrect=.90, attributeWeights=c(1,1,1,1))
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.
sorterMapping( CMData, numberOfSorterClusters = "auto", verbose = TRUE, rangeNumberOfClusters = 2:15, graph = TRUE )sorterMapping( CMData, numberOfSorterClusters = "auto", verbose = TRUE, rangeNumberOfClusters = 2:15, graph = TRUE )
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 |
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.
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.
# 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)# 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)
This function provides a summary of a concept map object, including the number of statements, sorters, clusters, and the distribution of statements across clusters.
## S3 method for class 'conceptMap' summary(object, ...)## S3 method for class 'conceptMap' summary(object, ...)
object |
An object of class |
... |
arguments to be passed to methods |
The function verifies that the input object is of class "conceptMap" and extracts key information from it.
It summarizes the number of statements, sorters, and clusters, and details the distribution of statements across
the identified clusters.
This function does not return a value; it prints a summary of the concept map object to the console.
# 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 3 to concept mapping using default "network" method myCMAnalysis3 <- conceptMapping(myCMDataBySorters[[3]]) # Generate summary of concept map of sorter cluster 3 summary(myCMAnalysis3)# 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 3 to concept mapping using default "network" method myCMAnalysis3 <- conceptMapping(myCMDataBySorters[[3]]) # Generate summary of concept map of sorter cluster 3 summary(myCMAnalysis3)