ca: Correspondence analysis

In sequence: Analysis of Sequences of Events

Description

Achieves a Correspondence Analysis (CA) on a numeric table of class data.frame

Usage

ca(x, nfac = 3, isup = 0, jsup = 0, histev = FALSE, grr = FALSE, grc = FALSE, 
grrc = FALSE, grlist = rbind(c(1, 2), c(1, 3), c(2, 3)), prtm = FALSE, 
prtevr = FALSE, prtevc = FALSE, eps = 1e-09)

Arguments

x	data.frame minimal dimension 4 x 3. The first column must contain the character strings of the identifiers of raws any other type, class or dimension results in an error and in the program break.
nfac	Number of factors to retain (maximum 7)
isup	list of illustrative rows. 0 = no illustrative rows (default)
jsup	List of illustrative columns. Same as isup.
histev	Boolean : whether to plot or not the histogram of eigenvalues.
grr	Boolean : plot the graph of rows on the axes defined by grlist.
grc	Boolean : Plot the graph of columns on the axes defined by grlist.
grrc	Boolean : Plot the simultaneous graph of rows and columns on the axes defined by grlist. Labels of rows in black, labels of columns in red.
grlist	matrix: defines the factorial plans to plot. See details for an example.
prtm	Boolean: Print or not the data frame. Default = FALSE
prtevr	Boolean: Print or not the rows eigenvectors. Default = FALSE
prtevc	Boolean: Print or not the columns eigenvectors. Default = FALSE
eps	numeric: (tolerance) Precision for null eigenvalues. Default = 10E-09

Details

grlist: the successive plots to draw are defined by a matrix of dimension k,2. k = number of plans to plot. Example: to plot the plans 1-2, 1-3 and 2-3 enter sometning as matrix(1,2,1,3,2,3,nrow=3,ncol=2,byrow=2) or rbind(c(1,2),c(1,3),c(2,3)). Markovian matrix: In the case of a Markovian or of a transition matrix, one can symetrise (X + t(X)) and load it (sum of the margins added to the diagonal, before applying CA (cf See Also).
In the case of a markovian square matrix (succession or transition matrix) one can symmetrize and load it (symet) before representing it by a graph (flux)

Value

An object of class ca with attributes

fr	data.frame: weight and factorial coordinates of each row (principal and illustrative). The attribute type has the value "pri" for principal and "ill" for illustrative
fc	data.frame: weight and factorial coordinates of each column (principal and illustrative). type as in fr

Author(s)

Jean-Sebastien Pierre [e-mail]

References

Van der Heijden, P. G. M. 1986. Transition matrices, model fitting and correspondence analysis. In: Data Analysis and Informatics IV (Ed. by E. Diday), pp. 221-226. Elsevier Science Publishers.

See Also

princomp, compseq to build a transition matrix,
symet to modify it (symmetrization and diagonal loading), flux for the design of a graph.

Examples

# On Csa data (xcsa)
library(sequence)
   data(xcsa)
   ca(xcsa)

sequence documentation built on March 26, 2020, 7:30 p.m.