| Title: | Clustering of Datasets |
| Version: | 4.1.1 |
| Maintainer: | Fabien Llobell <fabienllobellresearch@gmail.com> |
| Description: | 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. |
| Depends: | R (≥ 3.4.0) |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | FactoMineR |
| Suggests: | ClustVarLV |
| Date: | 2025-06-11 |
| RoxygenNote: | 7.2.3 |
| NeedsCompilation: | no |
| Packaged: | 2025-06-11 14:55:05 UTC; fllobell |
| Author: | Fabien Llobell [aut, cre] (Oniris/XLSTAT), Evelyne Vigneau [ctb] (Oniris), Veronique Cariou [ctb] (Oniris), El Mostafa Qannari [ctb] (Oniris) |
| Repository: | CRAN |
| Date/Publication: | 2025-06-11 15:10:06 UTC |
Clustering of Datasets
Description
Hierarchical and partitioning algorithms of blocks of variables.The CLUSTATIS method and the CLUSCATA method 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.
Details
| Package: | ClustBlock |
| Type: | Package |
| Version: | 4.1.1 |
| First version Date: | 2019-03-06 |
| Last version Date: | 2025-06-11 |
Author(s)
Fabien Llobell, Evelyne Vigneau, Veronique Cariou, El Mostafa Qannari
Maintainer: fabienllobellresearch@gmail.com
References
| Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2020). Analysis and clustering of multiblock datasets |
| by means of the STATIS and CLUSTATIS methods. Application to sensometrics. Food Quality and Preference, |
| 79, 103520. |
| Llobell, F., Vigneau, E., & Qannari, E. M. (2019). Clustering datasets by means of CLUSTATIS with identification |
| of atypical datasets. Application to sensometrics. Food Quality and Preference, 75, 97-104. |
| Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2019). A new approach for the analysis of data |
| and the clustering of subjects in a CATA experiment. Food quality and preference, 72, 31-39. |
| Llobell, F., Giacalone, D., Labenne, A., & Qannari, E. M. (2019). Assessment of the agreement and cluster analysis of |
| the respondents in a CATA experiment. Food Quality and Preference, 77, 184-190. |
| Llobell, F., & Qannari, E. M. (2020). CLUSTATIS: Cluster analysis of blocks of variables. Electronic |
| Journal of Applied Statistical Analysis, 13(2), 436-453. |
| Llobell, F. (2020). Classification de tableaux de donnees, applications en analyse sensorielle (Doctoral |
| dissertation, Nantes, Ecole nationale veterinaire). |
| Llobell, F., Bonnet, L., & Giacalone, D. (2024). Assessment of panel performance in CATA and RATA experiment. |
| Journal of Sensory Studies, 39(4), e12941. |
| Llobell, F., & Giacalone, D. (2025). Two Methods for Clustering Products in a Sensory Study: STATIS and ClusMB. |
| Journal of Sensory Studies, 40(1), e70024. |
Perform a cluster analysis of rows in a Multi-block context with the ClusMB method
Description
Clustering of rows (products in sensory analysis) in a Multi-block context. The hierarchical clustering is followed by a partitioning algorithm (consolidation).
Usage
ClusMB(Data, Blocks, NameBlocks=NULL, scale=FALSE, center=TRUE,
nclust=NULL, gpmax=6)
Arguments
Data |
data frame or matrix. Correspond to all the blocks of variables merged horizontally |
Blocks |
numerical vector. The number of variables of each block. The sum must be equal to the number of columns of Data. |
NameBlocks |
string vector. Name of each block. Length must be equal to the length of Blocks vector. If NULL, the names are B1,...Bm. Default: NULL |
scale |
logical. Should the data variables be scaled? Default: FALSE |
center |
logical. Should the data variables be centered? Default: TRUE. Please set to FALSE for a CATA experiment |
nclust |
numerical. Number of clusters to consider. If NULL, the Hartigan index advice is taken. |
gpmax |
logical. What is maximum number of clusters to consider? Default: min(6, number of blocks -2) |
Value
group: the clustering partition after consolidation.
nbgH: Advised number of clusters per Hartigan index
nbgCH: Advised number of clusters per Calinski-Harabasz index
cutree_k: the partition obtained by cutting the dendrogram in K clusters (before consolidation).
dend: The ClusMB dendrogram
param: parameters called
type: parameter passed to other functions
References
Llobell, F., & Giacalone, D. (2025). Two Methods for Clustering Products in a Sensory Study: STATIS and ClusMB. Journal of Sensory Studies, 40(1), e70024.
Llobell, F., Qannari, E.M. (June 10, 2022). Cluster analysis in a multi-bloc setting. SMTDA, Athens, Greece.
Llobell, F., Giacalone, D., Qannari, E. M. (Pangborn 2021). Cluster Analysis of products in CATA experiments.
See Also
indicesClusters, summary.clusRows , clustRowsOnStatisAxes
Examples
#####projective mapping####
library(ClustBlock)
data(smoo)
res1=ClusMB(smoo, rep(2,24))
summary(res1)
indicesClusters(smoo, rep(2,24), res1$group)
####CATA####
data(fish)
Data=fish[1:66,2:30]
chang2=change_cata_format2(Data, nprod= 6, nattr= 27, nsub = 11, nsess= 1)
res2=ClusMB(Data= chang2$Datafinal, Blocks= rep(27, 11), center=FALSE)
indicesClusters(Data= chang2$Datafinal, Blocks= rep(27, 11),cut = res2$group, center=FALSE)
RATA data on chocolates
Description
RATA data on chocolates
Usage
data(RATAchoc)
Format
RATA data with sessions. A data frame with 3 sessions, 9 panelists, 12 products and 27 RATA attributes.
References
Pangborn 2023
Examples
data(RATAchoc)
Perform the CATATIS method on different blocks from a CATA experiment
Description
CATATIS method. Additional outputs are also computed. Non-binary data are accepted and weights can be tested.
Usage
catatis(Data,nblo,NameBlocks=NULL, NameVar=NULL, Graph=TRUE, Graph_weights=TRUE,
Test_weights=FALSE, nperm=100)
Arguments
Data |
data frame or matrix where the blocks of binary variables are merged horizontally. If you have a different format, see |
nblo |
integer. Number of blocks (subjects). |
NameBlocks |
string vector. Name of each block (subject). Length must be equal to the number of blocks. If NULL, the names are S1,...Sm. Default: NULL |
NameVar |
string vector. Name of each variable (attribute, the same names for each subject). Length must be equal to the number of attributes. If NULL, the colnames of the first block are taken. Default: NULL |
Graph |
logical. Show the graphical representation? Default: TRUE |
Graph_weights |
logical. Should the barplot of the weights be plotted? Default: TRUE |
Test_weights |
logical. Should the the weights be tested? Default: FALSE |
nperm |
integer. Number of permutation for the weight tests. Default: 100 |
Value
a list with:
S: the S matrix: a matrix with the similarity coefficient among the subjects
compromise: a matrix which is the compromise of the subjects (akin to a weighted average)
weights: the weights associated with the subjects to build the compromise
weights_tests: the weights tests results
lambda: the first eigenvalue of the S matrix
overall error: the error for the CATATIS criterion
error_by_sub: the error by subject (CATATIS criterion)
error_by_prod: the error by product (CATATIS criterion)
s_with_compromise: the similarity coefficient of each subject with the compromise
homogeneity: homogeneity of the subjects (in percentage)
CA: the results of correspondence analysis performed on the compromise dataset
eigenvalues: the eigenvalues associated to the correspondence analysis
inertia: the percentage of total variance explained by each axis of the CA
scalefactors: the scaling factors of each subject
nb_1: the number of 1 in each block, i.e. the number of checked attributes by subject.
param: parameters called
References
Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2019). A new approach for the analysis of data and the clustering of subjects in a CATA experiment. Food Quality and Preference, 72, 31-39.
Bonnet, L., Ferney, T., Riedel, T., Qannari, E.M., Llobell, F. (September 14, 2022) .Using CATA for sensory profiling: assessment of the panel performance. Eurosense, Turku, Finland.
See Also
plot.catatis, summary.catatis, cluscata, change_cata_format, change_cata_format2
Examples
data(straw)
res.cat=catatis(straw, nblo=114)
summary(res.cat)
plot(res.cat)
#Vertical format with sessions
data("fish")
chang=change_cata_format2(fish, nprod= 6, nattr= 27, nsub = 12, nsess= 3)
res.cat2=catatis(Data= chang$Datafinal, nblo = 12, NameBlocks = chang$NameSub, Test_weights=TRUE)
#Vertical format without sessions
Data=fish[1:66,2:30]
chang2=change_cata_format2(Data, nprod= 6, nattr= 27, nsub = 11, nsess= 1)
res.cat3=catatis(Data= chang2$Datafinal, nblo = 11, NameBlocks = chang2$NameSub)
Perform the CATATIS method on Just About Right data.
Description
CATATIS method adapted to JAR data.
Usage
catatis_jar(Data, nprod, nsub, levelsJAR=3, beta=0.1, Graph=TRUE, Graph_weights=TRUE,
Test_weights=FALSE, nperm=100)
Arguments
Data |
data frame where the first column is the Assessors, the second is the products and all other columns the JAR attributes with numbers (1 to 3 or 1 to 5, see levelsJAR) |
nprod |
integer. Number of products. |
nsub |
integer. Number of subjects. |
levelsJAR |
integer. 3 or 5 levels. If 5, the data will be transformed in 3 levels. |
beta |
numerical. Parameter for agreement between JAR and other answers. Between 0 and 0.5. |
Graph |
logical. Show the graphical representation? Default: TRUE |
Graph_weights |
logical. Should the barplot of the weights be plotted? Default: TRUE |
Test_weights |
logical. Should the the weights be tested? Default: FALSE |
nperm |
integer. Number of permutation for the weight tests. Default: 100 |
Value
a list with:
S: the S matrix: a matrix with the similarity coefficient among the subjects
compromise: a matrix which is the compromise of the subjects (akin to a weighted average)
weights: the weights associated with the subjects to build the compromise
weights_tests: the weights tests results
lambda: the first eigenvalue of the S matrix
overall error: the error for the CATATIS criterion
error_by_sub: the error by subject (CATATIS criterion)
error_by_prod: the error by product (CATATIS criterion)
s_with_compromise: the similarity coefficient of each subject with the compromise
homogeneity: homogeneity of the subjects (in percentage)
CA: the results of correspondance analysis performed on the compromise dataset
eigenvalues: the eigenvalues associated to the correspondance analysis
inertia: the percentage of total variance explained by each axis of the CA
scalefactors: the scaling factors of each subject
nb_1: Can be ignored
param: parameters called
References
Llobell, F., Vigneau, E. & Qannari, E. M. ((September 14, 2022). Multivariate data analysis and clustering of subjects in a Just about right task. Eurosense, Turku, Finland.
See Also
catatis, plot.catatis, summary.catatis, cluscata_jar, preprocess_JAR, cluscata_kmeans_jar
Examples
data(cheese)
res.cat=catatis_jar(Data=cheese, nprod=8, nsub=72, levelsJAR=5)
summary(res.cat)
#plot(res.cat)
Perform the CATATIS method on different blocks from a RATA experiment
Description
CATATIS method for RATA data. Additional outputs are also computed. Non-binary data are accepted and weights can be tested.
Usage
catatis_rata(Data,nblo,NameBlocks=NULL, NameVar=NULL, Graph=TRUE, Graph_weights=TRUE,
Test_weights=FALSE, nperm=100)
Arguments
Data |
data frame or matrix where the blocks of variables are merged horizontally. If you have a different format, see |
nblo |
integer. Number of blocks (subjects). |
NameBlocks |
string vector. Name of each block (subject). Length must be equal to the number of blocks. If NULL, the names are S1,...Sm. Default: NULL |
NameVar |
string vector. Name of each variable (attribute, the same names for each subject). Length must be equal to the number of attributes. If NULL, the colnames of the first block are taken. Default: NULL |
Graph |
logical. Show the graphical representation? Default: TRUE |
Graph_weights |
logical. Should the barplot of the weights be plotted? Default: TRUE |
Test_weights |
logical. Should the the weights be tested? Default: FALSE |
nperm |
integer. Number of permutation for the weight tests. Default: 100 |
Value
a list with:
S: the S matrix: a matrix with the similarity coefficient among the subjects
compromise: a matrix which is the compromise of the subjects (akin to a weighted average)
weights: the weights associated with the subjects to build the compromise
weights_tests: the weights tests results
lambda: the first eigenvalue of the S matrix
overall error: the error for the CATATIS criterion
error_by_sub: the error by subject (CATATIS criterion)
error_by_prod: the error by product (CATATIS criterion)
s_with_compromise: the similarity coefficient of each subject with the compromise
homogeneity: homogeneity of the subjects (in percentage)
CA: the results of correspondence analysis performed on the compromise dataset
eigenvalues: the eigenvalues associated to the correspondence analysis
inertia: the percentage of total variance explained by each axis of the CA
scalefactors: the scaling factors of each subject
param: parameters called
References
Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2019). A new approach for the analysis of data and the clustering of subjects in a CATA experiment. Food Quality and Preference, 72, 31-39.
Bonnet, L., Ferney, T., Riedel, T., Qannari, E.M., Llobell, F. (September 14, 2022) .Using CATA for sensory profiling: assessment of the panel performance. Eurosense, Turku, Finland.
Bonnet, L., Llobell, F., Qannari, E.M. (Pangborn 2023). Assessment of the panel performance in a RATA experiment.
See Also
catatis, plot.catatis, summary.catatis, change_cata_format, change_cata_format2
Examples
#RATA data with session
data(RATAchoc)
chang2=change_cata_format2(RATAchoc, nprod= 12, nattr= 13, nsub = 9, nsess= 3)
res.cat4=catatis_rata(Data= chang2$Datafinal, nblo = 9, NameBlocks = chang2$NameSub)
summary(res.cat4)
#RATA data without session
Data=RATAchoc[1:108,2:16]
chang2=change_cata_format2(Data, nprod= 12, nattr= 13, nsub = 9, nsess = 1)
res.cat5=catatis_rata(Data= chang2$Datafinal, nblo = 9, NameBlocks = chang2$NameSub)
summary(res.cat5)
graphics.off()
Change format of CATA datasets to perform CATATIS or CLUSCATA function
Description
CATATIS and CLUSCATA operate on data where the blocksvariables are merged horizontally. If you have a different format, you can use this function to change the format. Format=1 is for data merged vertically with the dataset of the first subject, then the second,... with products in same order Format=2 is for data merged vertically with the dataset for the first product, then the second... with subjects in same order
Unlike change_cata_format2, you don't need to specify products and subjects, just make sure they are in the right order.
Usage
change_cata_format(Data, nprod, nattr, nsub, format=1, NameProds=NULL, NameAttr=NULL)
Arguments
Data |
data frame or matrix. Correspond to your data |
nprod |
integer. Number of products |
nattr |
integer. Number of attributes |
nsub |
integer. Number of subjects. |
format |
integer (1 or 2). See the description |
NameProds |
string vector with the names of the products (length must be nprod) |
NameAttr |
string vector with the names of attributes (length must be nattr) |
Value
The arranged data for CATATIS and CLUSCATA function
See Also
catatis, cluscata, change_cata_format2
Change format of CATA datasets to perform the package functions
Description
CATATIS and CLUSCATA operate on data where the blocks of variables are merged horizontally. If you have a vertical format, you can use this function to change the format. The first column must contain the sessions, the second the subjects, the third the products and the others the attributes. If you don't have sessions, then the first column must contain the subjects and the second the products. Unlike change_cata_format function, you can enter data with sessions and/or mixed data in terms of products/subjects. However, you have to set columns to indicate this beforehand.
Usage
change_cata_format2(Data, nprod, nattr, nsub, nsess)
Arguments
Data |
data frame or matrix. Correspond to your data |
nprod |
integer. Number of products |
nattr |
integer. Number of attributes |
nsub |
integer. Number of subjects. |
nsess |
integer. Number of sessions |
Value
The arranged data for CATATIS and CLUSCATA function and the subjects names in the correct order.
See Also
catatis, cluscata, change_cata_format
Examples
#Vertical format with sessions
data("fish")
chang=change_cata_format2(fish, nprod= 6, nattr= 27, nsub = 12, nsess= 3)
res.cat2=catatis(Data= chang$Datafinal, nblo = 12, NameBlocks = chang$NameSub)
#Vertical format without sessions
Data=fish[1:66,2:30]
chang2=change_cata_format2(Data, nprod= 6, nattr= 27, nsub = 11, nsess= 1)
res.cat3=catatis(Data= chang2$Datafinal, nblo = 11, NameBlocks = chang2$NameSub)
res.clu3=cluscata(Data= chang2$Datafinal, nblo = 11, NameBlocks = chang2$NameSub)
cheese Just About Right data
Description
cheese Just About Right data
Usage
data(cheese)
Format
JAR data. A data frame with Assessors, Products and JAR attributes. 8 products, 9 attributes and 72 subjects.
References
Luc, A., Lê, S., Philippe, M., Qannari, E. M., & Vigneau, E. (2022). Free JAR experiment: Data analysis and comparison with JAR task. Food Quality and Preference, 98, 104453.
Examples
data(cheese)
chocolates data
Description
chocolates data
Usage
data(choc)
Format
Free sorting data. A data frame with 14 rows (the chocolates) and 25 columns (the subjects). The numbers indicate the groups to which the products (rows) are assigned.
References
Courcoux, P., Qannari, E. M., Taylor, Y., Buck, D., & Greenhoff, K. (2012). Taxonomic free sorting. Food Quality and Preference, 23(1), 30-35.
Examples
data(choc)
Perform a cluster analysis of subjects from a CATA experiment
Description
Clustering of subjects (blocks) from a CATA experiment. Each cluster of blocks is associated with a compromise computed by the CATATIS method. The hierarchical clustering is followed by a partitioning algorithm (consolidation). Non-binary data are accepted.
Usage
cluscata(Data, nblo, NameBlocks=NULL, NameVar=NULL, Noise_cluster=FALSE,
Unique_threshold=TRUE, Itermax=30, Graph_dend=TRUE, Graph_bar=TRUE,
printlevel=FALSE, gpmax=min(6, nblo-2), rhoparam=NULL,
Testonlyoneclust=FALSE, alpha=0.05, nperm=50, Warnings=FALSE)
Arguments
Data |
data frame or matrix where the blocks of binary variables are merged horizontally. If you have a different format, see |
nblo |
numerical. Number of blocks (subjects). |
NameBlocks |
string vector. Name of each block (subject). Length must be equal to the number of blocks. If NULL, the names are S1,...Sm. Default: NULL |
NameVar |
string vector. Name of each variable (attribute, the same names for each subject). Length must be equal to the number of attributes. If NULL, the colnames of the first block are taken. Default: NULL |
Noise_cluster |
logical. Should a noise cluster be computed? Default: FALSE |
Unique_threshold |
logical. Use same rho for every cluster? Default: TRUE |
Itermax |
numerical. Maximum of iteration for the partitioning algorithm. Default:30 |
Graph_dend |
logical. Should the dendrogram be plotted? Default: TRUE |
Graph_bar |
logical. Should the barplot of the difference of the criterion and the barplot of the overall homogeneity at each merging step of the hierarchical algorithm be plotted? Default: TRUE |
printlevel |
logical. Print the number of remaining levels during the hierarchical clustering algorithm? Default: FALSE |
gpmax |
logical. What is maximum number of clusters to consider? Default: min(6, nblo-2) |
rhoparam |
numerical or vector. What is the threshold for the noise cluster? Between 0 and 1, high value can imply lot of blocks set aside. If NULL, automatic threshold is computed. Can be different for each group (in this case, provide a vector) |
Testonlyoneclust |
logical. Test if there is more than one cluster? Default: FALSE |
alpha |
numerical between 0 and 1. What is the threshold to test if there is more than one cluster? Default: 0.05 |
nperm |
numerical. How many permutations are required to test if there is more than one cluster? Default: 50 |
Warnings |
logical. Display warnings about the fact that none of the subjects in some clusters checked an attribute or product? Default: FALSE |
Value
Each partitionK contains a list for each number of clusters of the partition, K=1 to gpmax with:
group: the clustering partition after consolidation. If Noise_cluster=TRUE, some subjects could be in the noise cluster ("K+1")
rho: the threshold for the noise cluster
homogeneity: homogeneity index (
s_with_compromise: similarity coefficient of each subject with its cluster compromise
weights: weight associated with each subject in its cluster
compromise: the compromise of each cluster
CA: list. the correspondance analysis results on each cluster compromise (coordinates, contributions...)
inertia: percentage of total variance explained by each axis of the CA for each cluster
s_all_cluster: the similarity coefficient between each subject and each cluster compromise
criterion: the CLUSCATA criterion error
param: parameters called
type: parameter passed to other functions
There is also at the end of the list:
dend: The CLUSCATA dendrogram
cutree_k: the partition obtained by cutting the dendrogram in K clusters (before consolidation).
overall_homogeneity_ng: percentage of overall homogeneity by number of clusters before consolidation (and after if there is no noise cluster)
diff_crit_ng: variation of criterion when a merging is done before consolidation (and after if there is no noise cluster)
test_one_cluster: decision and pvalue to know if there is more than one cluster
param: parameters called
type: parameter passed to other functions
References
Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2019). A new approach for the analysis of data and the clustering of subjects in a CATA experiment. Food Quality and Preference, 72, 31-39.
Llobell, F., Giacalone, D., Labenne, A., Qannari, E.M. (2019). Assessment of the agreement and cluster analysis of the respondents in a CATA experiment. Food Quality and Preference, 77, 184-190.
See Also
plot.cluscata, summary.cluscata , catatis, cluscata_kmeans, change_cata_format, change_cata_format2
Examples
data(straw)
#with 40 subjects
res=cluscata(Data=straw[,1:(16*40)], nblo=40)
#plot(res, ngroups=3, Graph_dend=FALSE)
summary(res, ngroups=3)
#With noise cluster
res2=cluscata(Data=straw[,1:(16*40)], nblo=40, Noise_cluster=TRUE,
Graph_dend=FALSE, Graph_bar=FALSE)
#With noise cluster and defined rho threshold
#(high threshold for this example, you can put low threshold
#(ex: 0.2 or 0.3) to avoid set aside lot of respondents)
res3=cluscata(Data=straw[,1:(16*40)], nblo=40, Noise_cluster=TRUE,
Graph_dend=FALSE, Graph_bar=FALSE, rhoparam=0.6)
#different Noise cluster thresholds
res3=cluscata(Data=straw[,1:(16*40)], nblo=40, Noise_cluster=TRUE,
Graph_dend=FALSE, Graph_bar=FALSE, Unique_threshold= FALSE,
rhoparam=c(0.6, 0.5,0.4))
#with all subjects
res=cluscata(Data=straw, nblo=114, printlevel=TRUE)
#Vertical format
data("fish")
Data=fish[1:66,2:30]
chang2=change_cata_format2(Data, nprod= 6, nattr= 27, nsub = 11, nsess= 1)
res3=cluscata(Data= chang2$Datafinal, nblo = 11, NameBlocks = chang2$NameSub)
Perform a cluster analysis of subjects in a JAR experiment.
Description
Hierarchical clustering of subjects from a JAR experiment. Each cluster of subjects is associated with a compromise computed by the CATATIS method. The hierarchical clustering is followed by a partitioning algorithm (consolidation).
Usage
cluscata_jar(Data, nprod, nsub, levelsJAR=3, beta=0.1, Noise_cluster=FALSE,
Unique_threshold=TRUE, Itermax=30, Graph_dend=TRUE, Graph_bar=TRUE,
printlevel=FALSE, gpmax=min(6, nsub-2), rhoparam=NULL,
Testonlyoneclust=FALSE, alpha=0.05, nperm=50, Warnings=FALSE)
Arguments
Data |
data frame where the first column is the Assessors, the second is the products and all other columns the JAR attributes with numbers (1 to 3 or 1 to 5, see levelsJAR) |
nprod |
integer. Number of products. |
nsub |
integer. Number of subjects. |
levelsJAR |
integer. 3 or 5 levels. If 5, the data will be transformed in 3 levels. |
beta |
numerical. Parameter for agreement between JAR and other answers. Between 0 and 0.5. |
Noise_cluster |
logical. Should a noise cluster be computed? Default: FALSE |
Unique_threshold |
logical. Use same rho for every cluster? Default: TRUE |
Itermax |
numerical. Maximum of iteration for the partitioning algorithm. Default:30 |
Graph_dend |
logical. Should the dendrogram be plotted? Default: TRUE |
Graph_bar |
logical. Should the barplot of the difference of the criterion and the barplot of the overall homogeneity at each merging step of the hierarchical algorithm be plotted? Default: TRUE |
printlevel |
logical. Print the number of remaining levels during the hierarchical clustering algorithm? Default: FALSE |
gpmax |
logical. What is maximum number of clusters to consider? Default: min(6, nblo-2) |
rhoparam |
numerical or vector. What is the threshold for the noise cluster? Between 0 and 1, high value can imply lot of blocks set aside. If NULL, automatic threshold is computed. Can be different for each group (in this case, provide a vector) |
Testonlyoneclust |
logical. Test if there is more than one cluster? Default: FALSE |
alpha |
numerical between 0 and 1. What is the threshold to test if there is more than one cluster? Default: 0.05 |
nperm |
numerical. How many permutations are required to test if there is more than one cluster? Default: 50 |
Warnings |
logical. Display warnings about the fact that none of the subjects in some clusters checked an attribute or product? Default: FALSE |
Value
Each partitionK contains a list for each number of clusters of the partition, K=1 to gpmax with:
group: the clustering partition after consolidation. If Noise_cluster=TRUE, some subjects could be in the noise cluster ("K+1")
rho: the threshold(s) for the noise cluster
homogeneity: homogeneity index (
s_with_compromise: similarity coefficient of each subject with its cluster compromise
weights: weight associated with each subject in its cluster
compromise: the compromise of each cluster
CA: list. the correspondance analysis results on each cluster compromise (coordinates, contributions...)
inertia: percentage of total variance explained by each axis of the CA for each cluster
s_all_cluster: the similarity coefficient between each subject and each cluster compromise
criterion: the CLUSCATA criterion error
param: parameters called
type: parameter passed to other functions
There is also at the end of the list:
dend: The CLUSCATA dendrogram
cutree_k: the partition obtained by cutting the dendrogram in K clusters (before consolidation).
overall_homogeneity_ng: percentage of overall homogeneity by number of clusters before consolidation (and after if there is no noise cluster)
diff_crit_ng: variation of criterion when a merging is done before consolidation (and after if there is no noise cluster)
test_one_cluster: decision and pvalue to know if there is more than one cluster
param: parameters called
type: parameter passed to other functions
References
Llobell, F., Vigneau, E. & Qannari, E. M. ((September 14, 2022). Multivariate data analysis and clustering of subjects in a Just about right task. Eurosense, Turku, Finland.
See Also
plot.cluscata, summary.cluscata , catatis_jar, preprocess_JAR, cluscata_kmeans_jar
Examples
data(cheese)
res=cluscata_jar(Data=cheese, nprod=8, nsub=72, levelsJAR=5)
#plot(res, ngroups=4, Graph_dend=FALSE)
summary(res, ngroups=4)
Compute the CLUSCATA partitioning algorithm on different blocks from a CATA experiment
Description
Partitioning of binary Blocks from a CATA experiment. Each cluster is associated with a compromise computed by the CATATIS method. Can be performed using a multi-start strategy or initial partition provided by the user. Moreover, a noise cluster can be set up.
Usage
cluscata_kmeans(Data,nblo, clust, nstart=100, rho=0, NameBlocks=NULL, NameVar=NULL,
Itermax=30, Graph_groups=TRUE, print_attempt=FALSE, Warnings=FALSE)
Arguments
Data |
data frame or matrix where the blocks of binary variables are merged horizontally. If you have a different format, see |
nblo |
numerical. Number of blocks (subjects). |
clust |
numerical vector or integer. Initial partition or number of starting partitions if integer. If numerical vector, the numbers must be 1,2,3,...,number of clusters |
nstart |
numerical. Number of starting partitions. Default: 100 |
rho |
numerical or vector between 0 and 1. Threshold for the noise cluster. Default:0. If you want a different threshold for each cluster, you can provide a vector. |
NameBlocks |
string vector. Name of each block. Length must be equal to the number of blocks. If NULL, the names are S1,...Sm. Default: NULL |
NameVar |
string vector. Name of each variable (attribute, the same names for each subject). Length must be equal to the number of attributes. If NULL, the colnames of the first block are taken. Default: NULL |
Itermax |
numerical. Maximum of iterations by partitioning algorithm. Default: 30 |
Graph_groups |
logical. Should each cluster compromise graphical representation be plotted? Default: TRUE |
print_attempt |
logical. Print the number of remaining attempts in multi-start case? Default: FALSE |
Warnings |
logical. Display warnings about the fact that none of the subjects in some clusters checked an attribute or product? Default: FALSE |
Value
a list with:
group: the clustering partition. If rho>0, some subjects could be in the noise cluster ("K+1")
rho: the threshold for the noise cluster
homogeneity: percentage of homogeneity of the subjects in each cluster and the overall homogeneity
s_with_compromise: Similarity coefficient of each subject with its cluster compromise
weights: weight associated with each subject in its cluster
compromise: The compromise of each cluster
CA: The correspondance analysis results on each cluster compromise (coordinates, contributions...)
inertia: percentage of total variance explained by each axis of the CA for each cluster
s_all_cluster: the similarity coefficient between each subject and each cluster compromise
param: parameters called
criterion: the CLUSCATA criterion error
type: parameter passed to other functions
References
Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2019). A new approach for the analysis of data and the clustering of subjects in a CATA experiment. Food Quality and Preference, 72, 31-39.
Llobell, F., Giacalone, D., Labenne, A., Qannari, E.M. (2019). Assessment of the agreement and cluster analysis of the respondents in a CATA experiment. Food Quality and Preference, 77, 184-190.
See Also
plot.cluscata , summary.cluscata, catatis, cluscata, change_cata_format
Examples
data(straw)
cl_km=cluscata_kmeans(Data=straw[,1:(16*40)], nblo=40, clust=3)
#plot(cl_km, Graph_groups=FALSE, Graph_weights = TRUE)
summary(cl_km)
Perform a cluster analysis of subjects in a JAR experiment
Description
Partitioning of subject from a JAR experiment. Each cluster is associated with a compromise computed by the CATATIS method. Moreover, a noise cluster can be set up.
Usage
cluscata_kmeans_jar(Data, nprod, nsub, levelsJAR=3, beta=0.1, clust, nstart=100, rho=0,
Itermax=30, Graph_groups=TRUE, print_attempt=FALSE, Warnings=FALSE)
Arguments
Data |
data frame where the first column is the Assessors, the second is the products and all other columns the JAR attributes with numbers (1 to 3 or 1 to 5, see levelsJAR) |
nprod |
integer. Number of products. |
nsub |
integer. Number of subjects. |
levelsJAR |
integer. 3 or 5 levels. If 5, the data will be transformed in 3 levels. |
beta |
numerical. Parameter for agreement between JAR and other answers. Between 0 and 0.5. |
clust |
numerical vector or integer. Initial partition or number of starting partitions if integer. If numerical vector, the numbers must be 1,2,3,...,number of clusters |
nstart |
numerical. Number of starting partitions. Default: 100 |
rho |
numerical or vector between 0 and 1. Threshold for the noise cluster. Default:0. If you want a different threshold for each cluster, you can provide a vector. |
Itermax |
numerical. Maximum of iterations by partitioning algorithm. Default: 30 |
Graph_groups |
logical. Should each cluster compromise graphical representation be plotted? Default: TRUE |
print_attempt |
logical. Print the number of remaining attempts in multi-start case? Default: FALSE |
Warnings |
logical. Display warnings about the fact that none of the subjects in some clusters checked an attribute or product? Default: FALSE |
Value
a list with:
group: the clustering partition. If rho>0, some subjects could be in the noise cluster ("K+1")
rho: the threshold(s) for the noise cluster
homogeneity: percentage of homogeneity of the subjects in each cluster and the overall homogeneity
s_with_compromise: Similarity coefficient of each subject with its cluster compromise
weights: weight associated with each subject in its cluster
compromise: The compromise of each cluster
CA: The correspondance analysis results on each cluster compromise (coordinates, contributions...)
inertia: percentage of total variance explained by each axis of the CA for each cluster
s_all_cluster: the similarity coefficient between each subject and each cluster compromise
param: parameters called
criterion: the CLUSCATA criterion error
type: parameter passed to other functions
References
Llobell, F., Vigneau, E. & Qannari, E. M. ((September 14, 2022). Multivariate data analysis and clustering of subjects in a Just about right task. Eurosense, Turku, Finland.
See Also
plot.cluscata, summary.cluscata , catatis_jar, preprocess_JAR, cluscata_jar
Examples
data(cheese)
res=cluscata_kmeans_jar(Data=cheese, nprod=8, nsub=72, levelsJAR=5, clust=4)
#plot(res)
summary(res)
Perform a cluster analysis of subjects from a RATA experiment
Description
Hierarchical clustering of subjects (blocks) from a RATA experiment. Each cluster of blocks is associated with a compromise computed by the CATATIS method. The hierarchical clustering is followed by a partitioning algorithm (consolidation).
Usage
cluscata_rata(Data, nblo, NameBlocks=NULL, NameVar=NULL, Noise_cluster=FALSE,
Unique_threshold =TRUE, Itermax=30, Graph_dend=TRUE,
Graph_bar=TRUE, printlevel=FALSE,
gpmax=min(6, nblo-2), rhoparam=NULL, Testonlyoneclust=FALSE, alpha=0.05,
nperm=50, Warnings=FALSE)
Arguments
Data |
data frame or matrix where the blocks of binary variables are merged horizontally. If you have a different format, see |
nblo |
numerical. Number of blocks (subjects). |
NameBlocks |
string vector. Name of each block (subject). Length must be equal to the number of blocks. If NULL, the names are S1,...Sm. Default: NULL |
NameVar |
string vector. Name of each variable (attribute, the same names for each subject). Length must be equal to the number of attributes. If NULL, the colnames of the first block are taken. Default: NULL |
Noise_cluster |
logical. Should a noise cluster be computed? Default: FALSE |
Unique_threshold |
logical. Use same rho for every cluster? Default: TRUE |
Itermax |
numerical. Maximum of iteration for the partitioning algorithm. Default:30 |
Graph_dend |
logical. Should the dendrogram be plotted? Default: TRUE |
Graph_bar |
logical. Should the barplot of the difference of the criterion and the barplot of the overall homogeneity at each merging step of the hierarchical algorithm be plotted? Default: TRUE |
printlevel |
logical. Print the number of remaining levels during the hierarchical clustering algorithm? Default: FALSE |
gpmax |
logical. What is maximum number of clusters to consider? Default: min(6, nblo-2) |
rhoparam |
numerical or vector. What is the threshold for the noise cluster? Between 0 and 1, high value can imply lot of blocks set aside. If NULL, automatic threshold is computed. Can be different for each group (in this case, provide a vector) |
Testonlyoneclust |
logical. Test if there is more than one cluster? Default: FALSE |
alpha |
numerical between 0 and 1. What is the threshold to test if there is more than one cluster? Default: 0.05 |
nperm |
numerical. How many permutations are required to test if there is more than one cluster? Default: 50 |
Warnings |
logical. Display warnings about the fact that none of the subjects in some clusters checked an attribute or product? Default: FALSE |
Value
Each partitionK contains a list for each number of clusters of the partition, K=1 to gpmax with:
group: the clustering partition after consolidation. If Noise_cluster=TRUE, some subjects could be in the noise cluster ("K+1")
rho: the threshold(s) for the noise cluster
homogeneity: homogeneity index (
s_with_compromise: similarity coefficient of each subject with its cluster compromise
weights: weight associated with each subject in its cluster
compromise: the compromise of each cluster
CA: list. the correspondance analysis results on each cluster compromise (coordinates, contributions...)
inertia: percentage of total variance explained by each axis of the CA for each cluster
s_all_cluster: the similarity coefficient between each subject and each cluster compromise
criterion: the CLUSCATA criterion error
param: parameters called
type: parameter passed to other functions
There is also at the end of the list:
dend: The CLUSCATA dendrogram
cutree_k: the partition obtained by cutting the dendrogram in K clusters (before consolidation).
overall_homogeneity_ng: percentage of overall homogeneity by number of clusters before consolidation (and after if there is no noise cluster)
diff_crit_ng: variation of criterion when a merging is done before consolidation (and after if there is no noise cluster)
test_one_cluster: decision and pvalue to know if there is more than one cluster
param: parameters called
type: parameter passed to other functions
References
Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2019). A new approach for the analysis of data and the clustering of subjects in a CATA experiment. Food Quality and Preference, 72, 31-39.
Llobell, F., Giacalone, D., Labenne, A., Qannari, E.M. (2019). Assessment of the agreement and cluster analysis of the respondents in a CATA experiment. Food Quality and Preference, 77, 184-190.
Llobell, F., Jaeger, S.R. (September 11, 2024). Consumer segmentation based on sensory product characterisations elicited by RATA questions? Eurosense conference, Dublin, Ireland.
See Also
plot.cluscata, summary.cluscata , catatis_rata, change_cata_format, change_cata_format2
Examples
#RATA data without session
data(RATAchoc)
Data=RATAchoc[1:108,2:16]
chang2=change_cata_format2(Data, nprod= 12, nattr= 13, nsub = 9, nsess = 1)
res.clus=cluscata_rata(Data= chang2$Datafinal, nblo = 9, NameBlocks = chang2$NameSub)
summary(res.clus)
plot(res.clus)
Perform a cluster analysis of rows in a Multi-block context with clustering on STATIS axes
Description
Clustering of rows (products in sensory analysis) in a Multi-block context. The STATIS method is followed by a hierarchical algorithm.
Usage
clustRowsOnStatisAxes(Data, Blocks, NameBlocks=NULL, scale=FALSE,
nclust=NULL, gpmax=6, ncomp=5)
Arguments
Data |
data frame or matrix. Correspond to all the blocks of variables merged horizontally |
Blocks |
numerical vector. The number of variables of each block. The sum must be equal to the number of columns of Data. |
NameBlocks |
string vector. Name of each block. Length must be equal to the length of Blocks vector. If NULL, the names are B1,...Bm. Default: NULL |
scale |
logical. Should the data variables be scaled? Default: FALSE |
nclust |
numerical. Number of clusters to consider. If NULL, the Hartigan index advice is taken. |
gpmax |
logical. What is maximum number of clusters to consider? min(6, number of blocks -2) |
ncomp |
numerical. Number of axes to consider. Default:5 |
Value
group: the clustering partition.
nbgH: Advised number of clusters per Hartigan index
nbgCH: Advised number of clusters per Calinski-Harabasz index
cutree_k: the partition obtained by cutting the dendrogram in K clusters
dend: The dendrogram
param: parameters called
type: parameter passed to other functions
References
Llobell, F., & Giacalone, D. (2025). Two Methods for Clustering Products in a Sensory Study: STATIS and ClusMB. Journal of Sensory Studies, 40(1), e70024.
See Also
indicesClusters, summary.clusRows , ClusMB
Examples
#####projective mapping####
library(ClustBlock)
data(smoo)
res1=clustRowsOnStatisAxes(smoo, rep(2,24))
summary(res1)
indicesClusters(smoo, rep(2,24), res1$group)
####CATA####
data(fish)
Data=fish[1:66,2:30]
chang2=change_cata_format2(Data, nprod= 6, nattr= 27, nsub = 11, nsess= 1)
res2=clustRowsOnStatisAxes(Data= chang2$Datafinal, Blocks= rep(27, 11))
indicesClusters(Data= chang2$Datafinal, Blocks= rep(27, 11),cut = res2$group, center=FALSE)
Perform a cluster analysis of blocks of quantitative variables
Description
Hierarchical clustering of quantitative Blocks followed by a partitioning algorithm (consolidation). Each cluster of blocks is associated with a compromise computed by the STATIS method. Moreover, a noise cluster can be set up.
Usage
clustatis(Data,Blocks,NameBlocks=NULL,Noise_cluster=FALSE,
Unique_threshold=TRUE,scale=FALSE,
Itermax=30, Graph_dend=TRUE, Graph_bar=TRUE,
printlevel=FALSE, gpmax=min(6, length(Blocks)-2), rhoparam=NULL,
Testonlyoneclust=FALSE, alpha=0.05, nperm=50)
Arguments
Data |
data frame or matrix. Correspond to all the blocks of variables merged horizontally |
Blocks |
numerical vector. The number of variables of each block. The sum must be equal to the number of columns of Data |
NameBlocks |
string vector. Name of each block. Length must be equal to the length of Blocks vector. If NULL, the names are B1,...Bm. Default: NULL |
Noise_cluster |
logical. Should a noise cluster be computed? Default: FALSE |
Unique_threshold |
logical. Use same rho for every cluster? Default: TRUE |
scale |
logical. Should the data variables be scaled? Default: FALSE |
Itermax |
numerical. Maximum of iteration for the partitioning algorithm. Default: 30 |
Graph_dend |
logical. Should the dendrogram be plotted? Default: TRUE |
Graph_bar |
logical. Should the barplot of the difference of the criterion and the barplot of the overall homogeneity at each merging step of the hierarchical algorithm be plotted? Default: TRUE |
printlevel |
logical. Print the number of remaining levels during the hierarchical clustering algorithm? Default: FALSE |
gpmax |
logical. What is maximum number of clusters to consider? Default: min(6, number of blocks -2) |
rhoparam |
numerical or vector. What is the threshold for the noise cluster? Between 0 and 1, high value can imply lot of blocks set aside. If NULL, automatic threshold is computed. Can be different for each group (in this case, provide a vector) |
Testonlyoneclust |
logical. Test if there is more than one cluster? Default: FALSE |
alpha |
numerical between 0 and 1. What is the threshold to test if there is more than one cluster? Default: 0.05 |
nperm |
numerical. How many permutations are required to test if there is more than one cluster? Default: 50 |
Value
Each partitionK contains a list for each number of clusters of the partition, K=1 to gpmax with:
group: the clustering partition of datasets after consolidation. If Noise_cluster=TRUE, some blocks could be in the noise cluster ("K+1")
rho: the threshold(s) for the noise cluster (computed or input parameter)
homogeneity: homogeneity index (
rv_with_compromise: RV coefficient of each block with its cluster compromise
weights: weight associated with each block in its cluster
comp_RV: RV coefficient between the compromises associated with the various clusters
compromise: the W compromise of each cluster
coord: the coordinates of objects of each cluster
inertia: percentage of total variance explained by each axis for each cluster
rv_all_cluster: the RV coefficient between each block and each cluster compromise
criterion: the CLUSTATIS criterion error
param: parameters called in the consolidation
type: parameter passed to other functions
There is also at the end of the list:
dend: The CLUSTATIS dendrogram
cutree_k: the partition obtained by cutting the dendrogram for K clusters (before consolidation).
overall_homogeneity_ng: percentage of overall homogeneity by number of clusters before consolidation (and after if there is no noise cluster)
diff_crit_ng: variation of criterion when a merging is done before consolidation (and after if there is no noise cluster)
test_one_cluster: decision and pvalue to know if there is more than one cluster
param: parameters called
type: parameter passed to other functions
References
Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2018). Analysis and clustering of multiblock datasets by means of the STATIS and CLUSTATIS methods. Application to sensometrics. Food Quality and Preference, in Press.
Llobell, F., Vigneau, E., Qannari, E. M. (2019). Clustering datasets by means of CLUSTATIS with identification of atypical datasets. Application to sensometrics. Food Quality and Preference, 75, 97-104.
Llobell, F., & Qannari, E. M. (2020). CLUSTATIS: Cluster analysis of blocks of variables. Electronic Journal of Applied Statistical Analysis, 13(2).
See Also
plot.clustatis, summary.clustatis , clustatis_kmeans, statis
Examples
data(smoo)
NameBlocks=paste0("S",1:24)
cl=clustatis(Data=smoo,Blocks=rep(2,24),NameBlocks = NameBlocks)
#plot(cl, ngroups=3, Graph_dend=FALSE)
summary(cl)
#with noise cluster
cl2=clustatis(Data=smoo,Blocks=rep(2,24),NameBlocks = NameBlocks,
Noise_cluster=TRUE, Graph_dend=FALSE, Graph_bar=FALSE)
#with noise cluster and defined rho threshold
cl3=clustatis(Data=smoo,Blocks=rep(2,24),NameBlocks = NameBlocks,
Noise_cluster=TRUE, Graph_dend=FALSE, Graph_bar=FALSE, rhoparam=0.5)
#different Noise cluster thresholds
cl4=clustatis(Data=smoo,Blocks=rep(2,24),NameBlocks = NameBlocks,
Noise_cluster=TRUE, Graph_dend=FALSE, Graph_bar=FALSE, Unique_threshold= FALSE,
rhoparam=c(0.6, 0.5,0.4))
Perform a cluster analysis of free sorting data
Description
Hierarchical clustering of free sorting data followed by a partitioning algorithm (consolidation). Each cluster of blocks is associated with a compromise computed by the STATIS method. Moreover, a noise cluster can be set up.
Usage
clustatis_FreeSort(Data, NameSub=NULL, Noise_cluster=FALSE,
Unique_threshold = TRUE, Itermax=30,
Graph_dend=TRUE, Graph_bar=TRUE, printlevel=FALSE,
gpmax=min(6, ncol(Data)-1),rhoparam=NULL,
Testonlyoneclust=FALSE, alpha=0.05, nperm=50)
Arguments
Data |
data frame or matrix. Corresponds to all variables that contain subjects results. Each column corresponds to a subject and gives the groups to which the products (rows) are assigned |
NameSub |
string vector. Name of each subject. Length must be equal to the number of clumn of the Data. If NULL, the names are S1,...Sm. Default: NULL |
Noise_cluster |
logical. Should a noise cluster be computed? Default: FALSE |
Unique_threshold |
logical. Use same rho for every cluster? Default: TRUE |
Itermax |
numerical. Maximum of iteration for the partitioning algorithm. Default: 30 |
Graph_dend |
logical. Should the dendrogram be plotted? Default: TRUE |
Graph_bar |
logical. Should the barplot of the difference of the criterion and the barplot of the overall homogeneity at each merging be plotted? Default: FALSE |
printlevel |
logical. Print the number of remaining levels during the hierarchical clustering algorithm? Default: FALSE |
gpmax |
logical. What is maximum number of clusters to consider? Default: min(6, number of subjects -1) |
rhoparam |
numerical or vector. What is the threshold for the noise cluster? Between 0 and 1, high value can imply lot of blocks set aside. If NULL, automatic threshold is computed. Can be different for each group (in this case, provide a vector) |
Testonlyoneclust |
logical. Test if there is more than one cluster? Default: FALSE |
alpha |
numerical between 0 and 1. What is the threshold to test if there is more than one cluster? Default: 0.05 |
nperm |
numerical. How many permutations are required to test if there is more than one cluster? Default: 50 |
Value
Each partitionK contains a list for each number of clusters of the partition, K=1 to gpmax with:
group: the clustering partition of subjects after consolidation. If Noise_cluster=TRUE, some subjects could be in the noise cluster ("K+1")
rho: the threshold(s) for the noise cluster
homogeneity: homogeneity index (
rv_with_compromise: RV coefficient of each block with its cluster compromise
weights: weight associated with each subject in its cluster
comp_RV: RV coefficient between the compromises associated with the various clusters
compromise: the W compromise of each cluster
coord: the coordinates of objects of each cluster
inertia: percentage of total variance explained by each axis for each cluster
rv_all_cluster: the RV coefficient between each subject and each cluster compromise
criterion: the CLUSTATIS criterion error
param: parameters called in the consolidation
type: parameter passed to other functions
There is also at the end of the list:
dend: The CLUSTATIS dendrogram
cutree_k: the partition obtained by cutting the dendrogram for K clusters (before consolidation).
overall_homogeneity_ng: percentage of overall homogeneity by number of clusters before consolidation (and after if there is no noise cluster)
diff_crit_ng: variation of criterion when a merging is done before consolidation (and after if there is no noise cluster)
test_one_cluster: decision and pvalue to know if there is more than one cluster
param: parameters called
type: parameter passed to other functions
References
Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2018). Analysis and clustering of multiblock datasets by means of the STATIS and CLUSTATIS methods. Application to sensometrics. Food Quality and Preference, in Press.
Llobell, F., Vigneau, E., Qannari, E. M. (2019). Clustering datasets by means of CLUSTATIS with identification of atypical datasets. Application to sensometrics. Food Quality and Preference, 75, 97-104.
See Also
clustatis, preprocess_FreeSort, summary.clustatis, , plot.clustatis
Examples
data(choc)
res.clu=clustatis_FreeSort(choc)
plot(res.clu, Graph_dend=FALSE)
summary(res.clu)
Compute the CLUSTATIS partitioning algorithm on free sorting data
Description
partitioning algorithm for Free Sorting data. Each cluster is associated with a compromise computed by the STATIS method. Moreover, a noise cluster can be set up.
Usage
clustatis_FreeSort_kmeans(Data, NameSub=NULL, clust, nstart=100, rho=0,Itermax=30,
Graph_groups=TRUE, Graph_weights=FALSE, print_attempt=FALSE)
Arguments
Data |
data frame or matrix. Corresponds to all variables that contain subjects results. Each column corresponds to a subject and gives the groups to which the products (rows) are assigned |
NameSub |
string vector. Name of each subject. Length must be equal to the number of clumn of the Data. If NULL, the names are S1,...Sm. Default: NULL |
clust |
numerical vector or integer. Initial partition or number of starting partitions if integer. If numerical vector, the numbers must be 1,2,3,...,number of clusters |
nstart |
integer. Number of starting partitions. Default: 100 |
rho |
numerical or vector between 0 and 1. Threshold for the noise cluster. Default:0. If you want a different threshold for each cluster, you can provide a vector. |
Itermax |
numerical. Maximum of iterations by partitioning algorithm. Default: 30 |
Graph_groups |
logical. Should each cluster compromise be plotted? Default: TRUE |
Graph_weights |
logical. Should the barplot of the weights in each cluster be plotted? Default: FALSE |
print_attempt |
logical. Print the number of remaining attempts in the multi-start case? Default: FALSE |
Value
a list with:
group: the clustering partition. If rho>0, some subjects could be in the noise cluster ("K+1")
rho: the threshold(s) for the noise cluster
homogeneity: percentage of homogeneity of the subjects in each cluster and the overall homogeneity
rv_with_compromise: RV coefficient of each subject with its cluster compromise
weights: weight associated with each subject in its cluster
comp_RV: RV coefficient between the compromises associated with the various clusters
compromise: the W compromise of each cluster
coord: the coordinates of objects of each cluster
inertia: percentage of total variance explained by each axis for each cluster
rv_all_cluster: the RV coefficient between each subject and each cluster compromise
criterion: the CLUSTATIS criterion error
param: parameters called
type: parameter passed to other functions
References
Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2018). Analysis and clustering of multiblock datasets by means of the STATIS and CLUSTATIS methods. Application to sensometrics. Food Quality and Preference, in Press.
Llobell, F., Vigneau, E., Qannari, E. M. (2019). Clustering datasets by means of CLUSTATIS with identification of atypical datasets. Application to sensometrics. Food Quality and Preference, 75, 97-104.
See Also
clustatis_FreeSort, preprocess_FreeSort, summary.clustatis, , plot.clustatis
Examples
data(choc)
res.clu=clustatis_FreeSort_kmeans(choc, clust=2)
plot(res.clu, Graph_groups=FALSE, Graph_weights=TRUE)
summary(res.clu)
Compute the CLUSTATIS partitioning algorithm on different blocks of quantitative variables
Description
Partitioning algorithm for quantitative variables. Each cluster is associated with a compromise computed by the STATIS method. Can be performed using a multi-start strategy or initial partition provided by the user. Moreover, a noise cluster can be set up.
Usage
clustatis_kmeans(Data, Blocks, clust, nstart=100, rho=0, NameBlocks=NULL,
Itermax=30,Graph_groups=TRUE, Graph_weights=FALSE,
scale=FALSE, print_attempt=FALSE)
Arguments
Data |
data frame or matrix. Correspond to all the blocks of variables merged horizontally |
Blocks |
numerical vector. The number of variables of each block. The sum must be equal to the number of columns of Data |
clust |
numerical vector or integer. Initial partition or number of starting partitions if integer. If numerical vector, the numbers must be 1,2,3,...,number of clusters |
nstart |
integer. Number of starting partitions. Default: 100 |
rho |
numerical or vector between 0 and 1. Threshold for the noise cluster. Default:0. If you want a different threshold for each cluster, you can provide a vector. |
NameBlocks |
string vector. Name of each block. Length must be equal to the length of Blocks vector. If NULL, the names are B1,...Bm. Default: NULL |
Itermax |
numerical. Maximum of iterations by partitioning algorithm. Default: 30 |
Graph_groups |
logical. Should each cluster compromise be plotted? Default: TRUE |
Graph_weights |
logical. Should the barplot of the weights in each cluster be plotted? Default: FALSE |
scale |
logical. Should the data variables be scaled? Default: FALSE |
print_attempt |
logical. Print the number of remaining attempts in the multi-start case? Default: FALSE |
Value
a list with:
group: the clustering partition. If rho>0, some blocks could be in the noise cluster ("K+1")
rho: the threshold(s) for the noise cluster
homogeneity: percentage of homogeneity of the blocks in each cluster and the overall homogeneity
rv_with_compromise: RV coefficient of each block with its cluster compromise
weights: weight associated with each block in its cluster
comp_RV: RV coefficient between the compromises associated with the various clusters
compromise: the W compromise of each cluster
coord: the coordinates of objects of each cluster
inertia: percentage of total variance explained by each axis for each cluster
rv_all_cluster: the RV coefficient between each block and each cluster compromise
criterion: the CLUSTATIS criterion error
param: parameters called
type: parameter passed to other functions
References
Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2018). Analysis and clustering of multiblock datasets by means of the STATIS and CLUSTATIS methods. Application to sensometrics. Food Quality and Preference, in Press.
Llobell, F., Vigneau, E., Qannari, E. M. (2019). Clustering datasets by means of CLUSTATIS with identification of atypical datasets. Application to sensometrics. Food Quality and Preference, 75, 97-104.
See Also
plot.clustatis, clustatis, summary.clustatis, statis
Examples
data(smoo)
NameBlocks=paste0("S",1:24)
#with multi-start
cl_km=clustatis_kmeans(Data=smoo,Blocks=rep(2,24),NameBlocks = NameBlocks, clust=3)
#with an initial partition
cl=clustatis(Data=smoo,Blocks=rep(2,24),NameBlocks = NameBlocks,
Graph_dend=FALSE)
partition=cl$cutree_k$partition3
cl_km2=clustatis_kmeans(Data=smoo,Blocks=rep(2,24),NameBlocks = NameBlocks,
clust=partition, Graph_weights=FALSE, Graph_groups=FALSE)
graphics.off()
Test the consistency of each attribute in a CATA experiment
Description
Permutation test on the agreement between subjects for each attribute in a CATA experiment
Usage
consistency_cata(Data,nblo, nperm=100, alpha=0.05, printAttrTest=FALSE)
Arguments
Data |
data frame or matrix. Correspond to all the blocks of variables merged horizontally |
nblo |
numerical. Number of blocks (subjects). |
nperm |
numerical. How many permutations are required? Default: 100 |
alpha |
numerical between 0 and 1. What is the threshold? Default: 0.05 |
printAttrTest |
logical. Print the number of remaining attributes to be tested? Default: FALSE |
Value
a list with:
consist: the consistent attributes
no_consist: the inconsistent attributes
pval: pvalue for each test
References
Llobell, F., Giacalone, D., Labenne, A., Qannari, E.M. (2019). Assessment of the agreement and cluster analysis of the respondents in a CATA experiment. Food Quality and Preference, 77, 184-190.
See Also
consistency_cata_panel, change_cata_format, change_cata_format2
Examples
data(straw)
#with only 40 subjects
consistency_cata(Data=straw[,1:(16*40)], nblo=40)
#with all subjects
consistency_cata(Data=straw, nblo=114, printAttrTest=TRUE)
Test the consistency of the panel in a CATA experiment
Description
Permutation test on the agreement between subjects in a CATA experiment
Usage
consistency_cata_panel(Data,nblo, nperm=100, alpha=0.05)
Arguments
Data |
data frame or matrix. Correspond to all the blocks of variables merged horizontally |
nblo |
numerical. Number of blocks (subjects). |
nperm |
numerical. How many permutations are required? Default: 100 |
alpha |
numerical between 0 and 1. What is the threshold? Default: 0.05 |
Value
a list with:
answer: the answer of the test
pval: pvalue of the test
dis: distance between the homogeneity and the median of the permutations
References
Llobell, F., Giacalone, D., Labenne, A., Qannari, E.M. (2019). Assessment of the agreement and cluster analysis of the respondents in a CATA experiment. Food Quality and Preference, 77, 184-190.
Bonnet, L., Ferney, T., Riedel, T., Qannari, E.M., Llobell, F. (September 14, 2022) .Using CATA for sensory profiling: assessment of the panel performance. Eurosense, Turku, Finland.
See Also
consistency_cata, change_cata_format, change_cata_format2
Examples
data(straw)
#with all subjects
consistency_cata_panel(Data=straw, nblo=114)
fish data
Description
fish data
Usage
data(fish)
Format
CATA data with sessions. A data frame with the sessions, the panelists, the products and CATA attributes.
References
Bonnet, L., Ferney, T., Riedel, T., Qannari, E.M., Llobell, F. (September 14, 2022) .Using CATA for sensory profiling: assessment of the panel performance. Eurosense, Turku, Finland.
Examples
data(fish)
Compute the indices to evaluate the quality of the cluster partition in multi-block context
Description
Compute the Il index to evaluate the agreement between each block and the global partition (in sensory: agreement between each subject and the global partition)
Compute the Jl index to evaluate if each block has a partition (in sensory: if each subject made a partition of products)
Usage
indicesClusters(Data, Blocks, cut, NameBlocks=NULL, center=TRUE, scale=FALSE)
Arguments
Data |
data frame or matrix. Correspond to all the blocks of variables merged horizontally |
Blocks |
numerical vector. The number of variables of each block. The sum must be equal to the number of columns of Data. |
cut |
numerical vector. The partition of the cluster analysis. |
NameBlocks |
string vector. Name of each block. Length must be equal to the length of Blocks vector. If NULL, the names are B1,...Bm. Default: NULL |
center |
logical. Should the data variables be centered? Default: TRUE. Please set to FALSE for a CATA experiment |
scale |
logical. Should the data variables be scaled? Default: FALSE |
Value
Il: the Il indices
jl: the jl indicess
References
Llobell, F., & Giacalone, D. (2025). Two Methods for Clustering Products in a Sensory Study: STATIS and ClusMB. Journal of Sensory Studies, 40(1), e70024.
Llobell, F., Qannari, E.M. (June 10, 2022). Cluster analysis in a multi-bloc setting. SMTDA, Athens, Greece.
Llobell, F., Giacalone, D., Qannari, E. M. (Pangborn 2021). Cluster Analysis of products in CATA experiments.
See Also
Examples
#####projective mapping####
library(ClustBlock)
data(smoo)
res1=ClusMB(smoo, rep(2,24))
summary(res1)
indicesClusters(smoo, rep(2,24), res1$group)
####CATA####
data(fish)
Data=fish[1:66,2:30]
chang2=change_cata_format2(Data, nprod= 6, nattr= 27, nsub = 11, nsess= 1)
res2=ClusMB(Data= chang2$Datafinal, Blocks= rep(27, 11), center=FALSE)
indicesClusters(Data= chang2$Datafinal, Blocks= rep(27, 11),cut = res2$group, center=FALSE)
Displays the CATATIS graphs
Description
This function plots the CATATIS map and CATATIS weights
Usage
## S3 method for class 'catatis'
plot(x, Graph=TRUE, Graph_weights=TRUE, Graph_eig=TRUE,
axes=c(1,2), tit="CATATIS", cex=1, col.obj="blue", col.attr="red", ...)
Arguments
x |
object of class 'catatis' |
Graph |
logical. Show the graphical representation? Default: TRUE |
Graph_weights |
logical. Should the barplot of the weights be plotted? Default: TRUE |
Graph_eig |
logical. Should the barplot of the eigenvalues be plotted? Only with Graph=TRUE. Default: TRUE |
axes |
numerical vector (length 2). Axes to be plotted |
tit |
string. Title for the graphical representation. Default: 'CATATIS' |
cex |
numerical. Numeric character expansion factor; multiplied by par("cex") yields the final character size. NULL and NA are equivalent to 1.0. |
col.obj |
numerical or string. Color for the objects points. Default: "blue" |
col.attr |
numerical or string. Color for the attributes points. Default: "red" |
... |
further arguments passed to or from other methods |
Value
the CATATIS map
See Also
Examples
data(straw)
res.cat=catatis(straw, nblo=114)
plot(res.cat, Graph_weights=FALSE, axes=c(1,3))
Displays the ClusMB and clustRowsOnstatisAxes graphs
Description
This function plots the dendrogram of ClusMB or clustRowsOnstatisAxes
Usage
## S3 method for class 'clusRows'
plot(x, ...)
Arguments
x |
object of class 'clusRows' |
... |
further arguments passed to or from other methods |
Value
the dendrogram
See Also
Examples
##'
#####projective mapping####
library(ClustBlock)
data(smoo)
res1=ClusMB(smoo, rep(2,24))
plot(res1)
Displays the CLUSCATA graphs
Description
This function plots dendrogram, variation of the merging criterion, weights and CATATIS map of each cluster
Usage
## S3 method for class 'cluscata'
plot(x, ngroups=NULL, Graph_groups=TRUE, Graph_dend=TRUE,
Graph_bar=FALSE, Graph_weights=FALSE, axes=c(1,2), cex=1,
col.obj="blue", col.attr="red", ...)
Arguments
x |
object of class 'cluscata'. |
ngroups |
number of groups to consider. Ignored for cluscata_kmeans results. Default: recommended number of clusters |
Graph_groups |
logical. Should each cluster compromise graphical representation be plotted? Default: TRUE |
Graph_dend |
logical. Should the dendrogram be plotted? Default: TRUE |
Graph_bar |
logical. Should the barplot of the difference of the criterion and the barplot of the overall homogeneity at each merging step of the hierarchical algorithm be plotted? Also available after consolidation if Noise_cluster=FALSE. Default: FALSE |
Graph_weights |
logical. Should the barplot of the weights in each cluster be plotted? Default: FALSE |
axes |
numerical vector (length 2). Axes to be plotted. Default: c(1,2) |
cex |
numerical. Numeric character expansion factor; multiplied by par("cex") yields the final character size. NULL and NA are equivalent to 1.0. |
col.obj |
numerical or string. Color for the objects points. Default: "blue" |
col.attr |
numerical or string. Color for the attributes points. Default: "red" |
... |
further arguments passed to or from other methods |
Value
the CLUSCATA graphs
See Also
Examples
data(straw)
res=cluscata(Data=straw[,1:(16*40)], nblo=40)
plot(res, ngroups=3, Graph_dend=FALSE)
plot(res, ngroups=3, Graph_dend=FALSE,Graph_bar=FALSE, Graph_weights=FALSE, axes=c(1,3))
Displays the CLUSTATIS graphs
Description
This function plots dendrogram, variation of the merging criterion, weights and STATIS map of each cluster
Usage
## S3 method for class 'clustatis'
plot(x, ngroups=NULL, Graph_groups=TRUE, Graph_dend=TRUE,
Graph_bar=FALSE, Graph_weights=FALSE, axes=c(1,2), col=NULL, cex=1, font=1, ...)
Arguments
x |
object of class 'clustatis'. |
ngroups |
number of groups to consider. Ignored for clustatis_kmeans results. Default: recommended number of clusters |
Graph_groups |
logical. Should each cluster compromise graphical representation be plotted? Default: TRUE |
Graph_dend |
logical. Should the dendrogram be plotted? Default: TRUE |
Graph_bar |
logical. Should the barplot of the difference of the criterion and the barplot of the overall homogeneity at each merging step of the hierarchical algorithm be plotted? Also available after consolidation if Noise_cluster=FALSE. Default: FALSE |
Graph_weights |
logical. Should the barplot of the weights in each cluster be plotted? Default: FALSE |
axes |
numerical vector (length 2). Axes to be plotted. Default: c(1,2) |
col |
vector. Color for each object. Default: rainbow(nrow(Data)) |
cex |
numerical. Numeric character expansion factor; multiplied by par("cex") yields the final character size. NULL and NA are equivalent to 1.0. |
font |
numerical. Integer specifying font to use for text. 1=plain, 2=bold, 3=italic, 4=bold italic, 5=symbol. Default: 1 |
... |
further arguments passed to or from other methods |
Value
the CLUSTATIS graphs
See Also
Examples
data(smoo)
NameBlocks=paste0("S",1:24)
cl=clustatis(Data=smoo,Blocks=rep(2,24),NameBlocks = NameBlocks)
plot(cl, ngroups=3, Graph_dend=FALSE)
plot(cl, ngroups=3, Graph_dend=FALSE, axes=c(1,3))
graphics.off()
Display the STATIS charts
Description
This function plots the STATIS map and STATIS weights
Usage
## S3 method for class 'statis'
plot(x, axes=c(1,2), Graph_obj=TRUE,
Graph_weights=TRUE, Graph_eig=TRUE, tit="STATIS", col=NULL, cex=1, font=1,
xlim=NULL, ylim=NULL, ...)
Arguments
x |
object of class 'statis' |
axes |
numerical vector (length 2). Axes to be plotted. Default: c(1,2) |
Graph_obj |
logical. Should the compromise graphical representation be plotted? Default: TRUE |
Graph_weights |
logical. Should the barplot of the weights be plotted? Default: TRUE |
Graph_eig |
logical. Should the barplot of the eigenvalues be plotted? Only with Graph_obj=TRUE. Default: TRUE |
tit |
string. Title for the objects graphical representation. Default: 'STATIS' |
col |
vector. Color for each object. If NULL, col=rainbow(nrow(Data)). Default: NULL |
cex |
numerical. Numeric character expansion factor; multiplied by par("cex") yields the final character size. NULL and NA are equivalent to 1.0. |
font |
numerical. Integer specifying font to use for text. 1=plain, 2=bold, 3=italic, 4=bold italic, 5=symbol. Default: 1 |
xlim |
numerical vector (length 2). Minimum and maximum for x coordinates. |
ylim |
numerical vector (length 2). Minimum and maximum for y coordinates. |
... |
further arguments passed to or from other methods |
Value
the STATIS graphs
See Also
Examples
data(smoo)
NameBlocks=paste0("S",1:24)
st=statis(Data=smoo,Blocks=rep(2,24),NameBlocks = NameBlocks)
plot(st, axes=c(1,3), Graph_weights=FALSE)
Preprocessing for Free Sorting Data
Description
For Free Sorting Data, this preprocessing is needed.
Usage
preprocess_FreeSort(Data, NameSub=NULL)
Arguments
Data |
data frame or matrix. Corresponds to all variables that contain subjects results. Each column corresponds to a subject and gives the groups to which the products (rows) are assigned |
NameSub |
string vector. Name of each subject. Length must be equal to the number of clumn of the Data. If NULL, the names are S1,...Sm. Default: NULL |
Value
A list with:
new_Data: the Data transformed
Blocks: the number of groups for each subject
NameBlocks: the name of each subject
References
Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2018). Analysis and clustering of multiblock datasets by means of the STATIS and CLUSTATIS methods. Application to sensometrics. Food Quality and Preference, in Press.
See Also
Examples
data(choc)
prepro=preprocess_FreeSort(choc)
Preprocessing for Just About Right Data
Description
For JAR data, this preprocessing is needed.
Usage
preprocess_JAR(Data, nprod, nsub, levelsJAR=3, beta=0.1)
Arguments
Data |
data frame where the first column is the Assessors, the second is the products and all other columns the JAR attributes with numbers (1 to 3 or 1 to 5, see levelsJAR) |
nprod |
integer. Number of products. |
nsub |
integer. Number of subjects. |
levelsJAR |
integer. 3 or 5 levels. If 5, the data will be transformed in 3 levels. |
beta |
numerical. Parameter for agreement between JAR and other answers. Between 0 and 0.5. |
Value
A list with:
Datafinal: the Data transformed
NameSub: the name of each subject in the right order
References
Llobell, F., Vigneau, E. & Qannari, E. M. (September 14, 2022). Multivariate data analysis and clustering of subjects in a Just about right task. Eurosense, Turku, Finland.
See Also
catatis_jar, cluscata_jar, cluscata_kmeans_jar
Examples
data(cheese)
prepro=preprocess_JAR(cheese, nprod=8, nsub=72, levelsJAR=5)
Print the CATATIS results
Description
Print the CATATIS results
Usage
## S3 method for class 'catatis'
print(x, ...)
Arguments
x |
object of class 'catatis' |
... |
further arguments passed to or from other methods |
See Also
Print the ClusMB or clustering on STATIS axes results
Description
Print the ClusMB or clustering on STATIS axes results
Usage
## S3 method for class 'clusRows'
print(x, ...)
Arguments
x |
object of class 'clusRows' |
... |
further arguments passed to or from other methods |
See Also
Print the CLUSCATA results
Description
Print the CLUSCATA results
Usage
## S3 method for class 'cluscata'
print(x, ...)
Arguments
x |
object of class 'cluscata' |
... |
further arguments passed to or from other methods |
See Also
Print the CLUSTATIS results
Description
Print the CLUSTATIS results
Usage
## S3 method for class 'clustatis'
print(x, ...)
Arguments
x |
object of class 'clustatis' |
... |
further arguments passed to or from other methods |
See Also
Print the STATIS results
Description
Print the STATIS results
Usage
## S3 method for class 'statis'
print(x, ...)
Arguments
x |
object of class 'statis' |
... |
further arguments passed to or from other methods |
See Also
Testing the difference in perception between two predetermined groups of subjects in a CATA experiment
Description
Test adapted to CATA data to determine whether two predetermined groups of subjects have a different perception or not. For example, men and women.
Usage
simil_groups_cata(Data, groups, one=1, two=2, nperm=50, Graph=TRUE,
alpha= 0.05, printl=FALSE)
Arguments
Data |
data frame or matrix. Correspond to all the blocks of variables merged horizontally |
groups |
categorical vector. The groups of each subject . The length must be the number of subjects. |
one |
string. Name of the group 1 in groups vector. |
two |
string. Name of the group 2 in groups vector. |
nperm |
numerical. How many permutations are required? Default: 50 |
Graph |
logical. Should the CATATIS graph of each group be plotted? Default: TRUE |
alpha |
numerical between 0 and 1. What is the threshold of the test? Default: 0.05 |
printl |
logical. Print the number of remaining permutations during the algorithm? Default: FALSE |
Value
a list with:
decision: the decision of the test
pval: pvalue of the test
References
Llobell, F., Giacalone, D., Jaeger, S.R. & Qannari, E. M. (2021). CATA data: Are there differences in perception? JSM conference.
Llobell, F., Giacalone, D., Jaeger, S.R. & Qannari, E. M. (2021). CATA data: Are there differences in perception? AgroStat conference.
Examples
data(straw)
groups=sample(1:2, 114, replace=TRUE)
simil_groups_cata(straw, groups, one=1, two=2)
smoothies data
Description
smoothies data
Usage
data(smoo)
Format
Projective mapping (or Napping) data. A data frame with 8 rows (the number of smoothies) and 48 columns (the number of consumers * 2). For each consumer, we have the coordinates of the products on the sheet of paper.
References
Francois Husson, Sebastien Le and Marine Cadoret (2017). SensoMineR: Sensory Data Analysis. R package version 1.23. https://CRAN.R-project.org/package=SensoMineR
Examples
data(smoo)
Performs the STATIS method on different blocks of quantitative variables
Description
STATIS method on quantitative blocks. SUpplementary outputs are also computed
Usage
statis(Data,Blocks,NameBlocks=NULL,Graph_obj=TRUE, Graph_weights=TRUE, scale=FALSE)
Arguments
Data |
data frame or matrix. Correspond to all the blocks of variables merged horizontally |
Blocks |
numerical vector. The number of variables of each block. The sum must be equal to the number of columns of Data |
NameBlocks |
string vector. Name of each block. Length must be equal to the length of Blocks vector. If NULL, the names are B1,...Bm. Default: NULL |
Graph_obj |
logical. Show the graphical representation od the objects? Default: TRUE |
Graph_weights |
logical. Should the barplot of the weights be plotted? Default: TRUE |
scale |
logical. Should the data variables be scaled? Default: FALSE |
Value
a list with:
RV: the RV matrix: a matrix with the RV coefficient between blocks of variables
compromise: a matrix which is the compromise of the blocks (akin to a weighted average)
weights: the weights associated with the blocks to build the compromise
lambda: the first eigenvalue of the RV matrix
overall error : the error for the STATIS criterion
error_by_conf: the error by configuration (STATIS criterion)
rv_with_compromise: the RV coefficient of each block with the compromise
homogeneity: homogeneity of the blocks (in percentage)
coord: the coordinates of each object
eigenvalues: the eigenvalues of the svd decomposition
inertia: the percentage of total variance explained by each axis
error_by_obj: the error by object (STATIS criterion)
scalefactors: the scaling factors of each block
proj_config: the projection of each object of each configuration on the axes: presentation by configuration
proj_objects: the projection of each object of each configuration on the axes: presentation by object
References
Lavit, C., Escoufier, Y., Sabatier, R., Traissac, P. (1994). The act (statis method). Computational 462 Statistics & Data Analysis, 18 (1), 97-119.\
Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2018). Analysis and clustering of multiblock datasets by means of the STATIS and CLUSTATIS methods.Application to sensometrics. Food Quality and Preference, in Press.
See Also
Examples
data(smoo)
NameBlocks=paste0("S",1:24)
st=statis(Data=smoo, Blocks=rep(2,24),NameBlocks = NameBlocks)
#plot(st, axes=c(1,3))
summary(st)
#with variables scaling
st2=statis(Data=smoo, Blocks=rep(2,24),NameBlocks = NameBlocks, Graph_weights=FALSE, scale=TRUE)
Performs the STATIS method on Free Sorting data
Description
STATIS method on Free Sorting data. A lot of supplementary informations are also computed
Usage
statis_FreeSort(Data, NameSub=NULL, Graph_obj=TRUE, Graph_weights=TRUE)
Arguments
Data |
data frame or matrix. Corresponds to all variables that contain subjects results. Each column corresponds to a subject and gives the groups to which the products (rows) are assigned |
NameSub |
string vector. Name of each subject. Length must be equal to the number of clumn of the Data. If NULL, the names are S1,...Sm. Default: NULL |
Graph_obj |
logical. Show the graphical representation od the objects? Default: TRUE |
Graph_weights |
logical. Should the barplot of the weights be plotted? Default: TRUE |
Value
a list with:
a list with:
RV: the RV matrix: a matrix with the RV coefficient between subjects
compromise: a matrix which is the compromise of the subjects (akin to a weighted average)
weights: the weights associated with the subjects to build the compromise
lambda: the first eigenvalue of the RV matrix
overall error : the error for the STATIS criterion
error_by_conf: the error by configuration (STATIS criterion)
rv_with_compromise: the RV coefficient of each subject with the compromise
homogeneity: homogeneity of the subjects (in percentage)
coord: the coordinates of each object
eigenvalues: the eigenvalues of the svd decomposition
inertia: the percentage of total variance explained by each axis
error_by_obj: the error by object (STATIS criterion)
scalefactors: the scaling factors of each subject
proj_config: the projection of each object of each subject on the axes: presentation by subject
proj_objects: the projection of each object of each subject on the axes: presentation by object
References
Lavit, C., Escoufier, Y., Sabatier, R., Traissac, P. (1994). The act (statis method). Computational 462 Statistics & Data Analysis, 18 (1), 97-119.\
Llobell, F., Cariou, V., Vigneau, E., Labenne, A., & Qannari, E. M. (2018). Analysis and clustering of multiblock datasets by means of the STATIS and CLUSTATIS methods.Application to sensometrics. Food Quality and Preference, in Press.
See Also
preprocess_FreeSort, clustatis_FreeSort
Examples
data(choc)
res.sta=statis_FreeSort(choc)
strawberries data
Description
strawberries data
Usage
data(straw)
Format
CATA data. A data frame with 6 rows (the number of strawberries) and 1824 columns (the number of consumers (114) * the number of attributes (16)). For each consumer,each attribute and eachb product, there is 1 if the attribute has been checked by the consumer for the product, and 0 if not.
References
Ares, G., & Jaeger, S. R. (2013). Check-all-that-apply questions: Influence of attribute order on sensory product characterization. Food Quality and Preference, 28(1), 141-153.
Examples
data(straw)
Show the CATATIS results
Description
This function shows the CATATIS results
Usage
## S3 method for class 'catatis'
summary(object, ...)
Arguments
object |
object of class 'catatis'. |
... |
further arguments passed to or from other methods |
Value
a list with:
homogeneity: homogeneity of the subjects (in percentage)
weights: the weights associated with the subjects to build the compromise
eigenvalues: the eigenvalues associated to the correspondance analysis
inertia: the percentage of total variance explained by each axis of the CA
See Also
Show the ClusMB or clustering on STATIS axes results
Description
This function shows the ClusMB or clustering on STATIS axes results
Usage
## S3 method for class 'clusRows'
summary(object, ...)
Arguments
object |
object of class 'clusRows'. |
... |
further arguments passed to or from other methods |
Value
a list with:
groups: clustering partition
nbClustRetained: the number of clusters retained
nbgH: Advised number of clusters per Hartigan index
nbgCH: Advised number of clusters per Calinski-Harabasz index
See Also
Show the CLUSCATA results
Description
This function shows the cluscata results
Usage
## S3 method for class 'cluscata'
summary(object, ngroups=NULL, ...)
Arguments
object |
object of class 'cluscata'. |
ngroups |
number of groups to consider. Ignored for cluscata_kmeans results. Default: recommended number of clusters |
... |
further arguments passed to or from other methods |
Value
the CLUSCATA principal results
a list with:
group: the clustering partition
homogeneity: homogeneity index (
weights: weight associated with each subject in its cluster
rho: the threshold for the noise cluster
test_one_cluster: decision and pvalue to know if there is more than one cluster
See Also
Show the CLUSTATIS results
Description
This function shows the clustatis results
Usage
## S3 method for class 'clustatis'
summary(object, ngroups=NULL, ...)
Arguments
object |
object of class 'clustatis'. |
ngroups |
number of groups to consider. Ignored for clustatis_kmeans results. Default: recommended number of clusters |
... |
further arguments passed to or from other methods |
Value
the CLUSTATIS principal results
a list with:
group: the clustering partition
homogeneity: homogeneity index (
weights: weight associated with each block in its cluster
rho: the threshold for the noise cluster
test_one_cluster: decision and pvalue to know if there is more than one cluster
See Also
Show the STATIS results
Description
This function shows the STATIS results
Usage
## S3 method for class 'statis'
summary(object, ...)
Arguments
object |
object of class 'statis'. |
... |
further arguments passed to or from other methods |
Value
a list with:
homogeneity: homogeneity of the blocks (in percentage)
weights: the weights associated with the blocks to build the compromise
eigenvalues: the eigenvalues of the svd decomposition
inertia: the percentage of total variance explained by each axis