statis {ClustBlock}

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

plot.statis, clustatis

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)