cluscata {ClustBlock} | R Documentation |

Hierarchical clustering of blocks of binary data 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)

cluscata(Data, nblo, NameBlocks=NULL, NameVar=NULL, Noise_cluster=FALSE, Itermax=30, Graph_dend=TRUE, Graph_bar=TRUE, printlevel=FALSE, gpmax=min(6, nblo-2), Testonlyoneclust=TRUE, alpha=0.05, nperm=50, Warnings=FALSE)

`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 |

`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) |

`Testonlyoneclust` |
logical. Test if there is more than one cluster? Default: TRUE |

`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 |

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

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.

`plot.cluscata`

, `summary.cluscata`

, `catatis`

, `cluscata_kmeans`

, `change_cata_format`

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 all subjects res=cluscata(Data=straw, nblo=114, printlevel=TRUE)

[Package *ClustBlock* version 2.4.0 Index]