XploRe Help : corresp

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library:	stats
See also:

Quantlet:		corresp
Description:		corresp executes Correspondence Analysis which analyses and describes a contingency table cross-tabulations) in terms of a reduced number of dimensions. Correspondence Analysis can be viewed as finding the best simultaneous representation of two sets that comprise the rows and columns of a data matrix, in order to obtain a summary description for large tables (cross-tabulations). This technique can be helpful in finding important underlying characteristics which might not be directly observed in the data. Graphical visualizations provide an insight and understanding tool for interpreting the data.

Reference(s):

Lebart, L.; Morineau, A. and Piron, M. (1995): Statistique Exploratoire Multidimensionnelle. Dunod, Paris; page 67-103.

Link:

Correspondence Analysis

Usage:	`corresp(fadata,fsldata,fscdata,titl,fal,fac,fsl,fsc,outdoc)`
Input:
	`fadata`	name of active data (I x J matrix) file, Obligatory Parameter
	`fsldata`	name of supplementary row data (K x J matrix) file
	`fscdata`	name of supplementary column data (I x Q matrix) file
	`titl`	title of data set (text string)
	`fal`	name of row label (I x 1 string vector) file
	`fac`	name of column label (J x 1 string vector) file
	`fsl`	name of supplementary row label (K x 1 string vector) file
	`fsc`	name of supplementary column label (Q x 1 string vector) file
	`outdoc`	name of output file, default = "out.txt" that consists of Eigenvalues corresponding to the principal axes, Coordinates of items on the principal axes, Contributions of active items relative to a principal axis, Squared correlations which indicate the part of the variance of a variable explained by a principal axis, Relative weights (marginal frequencies) and Distances of items to the origin.

Example:

library("stats") corresp("yeux.dat")

Result:

You have the informations relative to the eigenvalues, coordinates of items
on the principal axes, ... etc in the file "yeux.txt".
You can also visualize the different items of "eye" and those of "hair" on graphs
with any two axes selected.

Example:

library("stats") corresp("mjob.dat")

Result:

You have all informations relative to the eigenvalues,
coordinates of items on the principal axes, ... etc in
the file "out.txt". You can visualize the active row
items labeled with the sequential numbers of rows
(color blue)  and active column items labeled with the
sequential numbers of columns (color green) on graphs.

Author: M. Benko, W. Haerdle, 20000417 license MD*Tech