statis {ClustBlock} | R Documentation |

STATIS method on quantitative blocks. SUpplementary outputs are also computed

statis(Data,Blocks,NameBlocks=NULL,Graph_obj=TRUE, Graph_weights=TRUE, scale=FALSE)

`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 |

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

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.

data(smoo) NameBlocks=paste0("S",1:24) st=statis(Data=smoo, Blocks=rep(2,24),NameBlocks = NameBlocks) summary(st) #with variables scaling st2=statis(Data=smoo, Blocks=rep(2,24),NameBlocks = NameBlocks, Graph_weights=FALSE, scale=TRUE)

[Package *ClustBlock* version 2.4.0 Index]