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.
See Also
preprocess_FreeSort
, clustatis_FreeSort
Examples
data(choc)
res.sta=statis_FreeSort(choc)