Clustering of Datasets
Perform the CATATIS method on Just About Right data.
Perform the CATATIS method on different blocks from a RATA experiment
Perform the CATATIS method on different blocks from a CATA experiment
Change format of CATA datasets to perform CATATIS or CLUSCATA function
Change format of CATA datasets to perform the package functions
Perform a cluster analysis of subjects in a JAR experiment.
Perform a cluster analysis of subjects in a JAR experiment
Compute the CLUSCATA partitioning algorithm on different blocks from a...
Perform a cluster analysis of subjects from a RATA experiment
Perform a cluster analysis of subjects from a CATA experiment
Perform a cluster analysis of rows in a Multi-block context with the C...
Compute the CLUSTATIS partitioning algorithm on free sorting data
Perform a cluster analysis of free sorting data
Compute the CLUSTATIS partitioning algorithm on different blocks of qu...
Perform a cluster analysis of blocks of quantitative variables
Clustering of Datasets
Perform a cluster analysis of rows in a Multi-block context with clust...
Test the consistency of the panel in a CATA experiment
Test the consistency of each attribute in a CATA experiment
Compute the indices to evaluate the quality of the cluster partition i...
Displays the CATATIS graphs
Displays the CLUSCATA graphs
Displays the ClusMB and clustRowsOnstatisAxes graphs
Displays the CLUSTATIS graphs
Display the STATIS charts
Preprocessing for Free Sorting Data
Preprocessing for Just About Right Data
Print the CATATIS results
Print the CLUSCATA results
Print the ClusMB or clustering on STATIS axes results
Print the CLUSTATIS results
Print the STATIS results
Testing the difference in perception between two predetermined groups ...
Performs the STATIS method on Free Sorting data
Performs the STATIS method on different blocks of quantitative variabl...
Show the CATATIS results
Show the CLUSCATA results
Show the ClusMB or clustering on STATIS axes results
Show the CLUSTATIS results
Show the STATIS results
Hierarchical and partitioning algorithms to cluster blocks of variables. The partitioning algorithm includes an option called noise cluster to set aside atypical blocks of variables. Different thresholds per cluster can be sets. The CLUSTATIS method (for quantitative blocks) (Llobell, Cariou, Vigneau, Labenne & Qannari (2020) <doi:10.1016/j.foodqual.2018.05.013>, Llobell, Vigneau & Qannari (2019) <doi:10.1016/j.foodqual.2019.02.017>) and the CLUSCATA method (for Check-All-That-Apply data) (Llobell, Cariou, Vigneau, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2018.09.006>, Llobell, Giacalone, Labenne & Qannari (2019) <doi:10.1016/j.foodqual.2019.05.017>) are the core of this package. The CATATIS methods allows to compute some indices and tests to control the quality of CATA data. Multivariate analysis and clustering of subjects for quantitative multiblock data, CATA, RATA, Free Sorting and JAR experiments are available. Clustering of rows in multi-block context (notably with ClusMB strategy) is also included.