pamctdp (version 0.3.0)

witwit.model: Within Correspondence Analysis using divers Models and Weights

Description

witwit.model performs an Double Within Tables Correspondence Analysis. Modification of witwit.coa of ade4 to allow Intra Block Model and divers weights

Usage

witwit.model(dudi, row.blocks, col.blocks, pfil = dudi$lw, pcol = dudi$cw, 
    model = "C", weight = "coa", scannf = TRUE, nf = 2,eps=1e-15,iter=100)
## S3 method for class 'wwmodel':
summary(object, \dots) 
## S3 method for class 'wwmodel':
print(x, \dots)

Arguments

dudi

an object of class coa

row.blocks

a numeric vector indicating the row numbers for each block of rows

col.blocks

a numeric vector indicating the column numbers for each block of columns

scannf

a logical value indicating whether the eigenvalues bar plot should be displayed

nf

if scannf FALSE, an integer indicating the number of kept axes

pfil

a numeric vector indicating the row weights

pcol

a numeric vector indicating the column weights

model

"C": the same model of ICA,"B": intra blocks independence model

weight

c("coa": the same row and columns weights than CA, "mfa": MFA-like weights in rows and columns, "mafc": MFA-like weights in columns, "mfar": MFA-like weights in rows

eps

convergence error if weight="mfa"

iter

maximum itection number if if weight="mfa"

object

an object of class wwmodel

x

an object of class wwmodel

...

further arguments passed to or from other methods

Value

• Returns a list of class wwmodel containing:
• tab
  a data frame with I rows and K columns
• cw
  column weights, a vector with K components
• lw
  row weights, a vector with I components
• eig
  eigenvalues, a vector with min(I,K) components
• nf
  integer, number of kept axes
• c1
  principal axes, data frame with I rows and nf columns
• l1
  principal components, data frame with I rows and nf columns
• co
  column coordinates, data frame with K rows and nf columns
• li
  row coordinates, data frame with I rows and nf columns
• call
  original call
• rbvar
  a data frame with the within variances of the rows of the factorial coordinates
• lbw
  a data frame with the marginal weighting of the row bands
• cvar
  a data frame with the within variances of the columns of the factorial coordinates
• cbw
  a data frame with the marginal weighting of the column bands
• hom
  homotecia to read some aids as in MFA
• rbl
  number of rows in each row-band
• cbl
  number of columns in each column-band
• sepeig.col
  band-column separate firt eigenvalues if weight="mfa"
• sepeig.row
  band-row separate firt eigenvalues if weight="mfa"

encoding

latin1

Details

This function is build up with witwit.coa of ade4, in order to allow diferents weights and models in a contingency table with double structure of partition. If model="C" and weight="coa" the results are the same of witwit.coa. If model="B" and weight="coa" a Intra-Blocks Correspondence Analysis (IBCA) is buld up If model="B" and weight="mfa" a Weighted Intra-Blocks Correspondence Analysis (WIBCA) is build up

References

Becue M., Pages J. and Pardo C.E. (2005). Contingency table with a double partition on rows and columns. Visualization and comparison of the partial and global structures. In: Proceedings ASMDA, Brest, France. May,17-20, 2005. Eds: Jacques Janssen and Philippe Lenca. ENST Bretagne. pages 355--364. http://conferences.telecom-bretagne.eu/asmda2005/IMG/pdf/proceedings/355.pdf

Cazes, P., Chessel, D. and Doledec, S. (1988) L'analyse des correspondances internes d'un tableau partitionne : son usage en hydrobiologie. Revue de Statistique Appliquee, 36, 39--54. http://archive.numdam.org/ARCHIVE/RSA/RSA_1988__36_1/RSA_1988__36_1_39_0/RSA_1988__36_1_39_0.pdf

Pardo, Campo El�as, M�nica B�cue-Bertaut, and Jorge Eduardo Ortiz. (2013). Correspondence Analysis of Contingency Tables with Subpartitions on Rows and Columns. Revista Colombiana de Estad�stica 36.1: 115--144. http://www.estadistica.unal.edu.co/publicaciones/estadistica/rce/V36/v36n1a07.pdf

Examples

data(ardeche)
# change column names
names(ardeche$tab) <- paste(ardeche$sta.fac,ardeche$dat.fac,sep="") 
rownames(ardeche$tab) <- # change row names
paste(strtrim(rownames(ardeche$tab),1),substr(rownames(ardeche$tab),4,
	length(rownames(ardeche$tab))),sep="")
coa1 <- dudi.coa(ardeche$tab, scannf = FALSE, nf = 4)
ww <- witwit.model(coa1, ardeche$row.blocks, ardeche$col.blocks, scann = FALSE)
ww
plot(ww)
summary(ww)