statis_FreeSort {ClustBlock} R Documentation

## 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.