partial.wwm: Coordinates and Aids to Interpretation of Superimposed...

In pamctdp: Principal Axes Methods for Contingency Tables with Partition Structures on Rows and Columns

Description

Coordinates and Aids to Interpretation of Superimposed Representation of Double Intra Correspondence Analysis

Usage

partial.wwm(ACww,dil = TRUE)
## S3 method for class 'parwwm'
print(x, ...)
"wwm.util.addfactor<-"(x,value)

Arguments

ACww	an object of class wwmodel
dil	when dil=TRUE the partial column coordinates are multiplied by the inve of the column band weight and the partial column coordinates are multiplied by the inverse of the column band weight but it is FALSE the multipliers are J and L

x	object of type parwwm
...	further arguments passed to or from other methods

value

list: rbl,cbl,nr,nc

Details

function 'wwm.util.addfactor<-' is used by 'partial.wwm' function

Value

comp1	Description of 'comp1'
comp2	Description of 'comp2'
dil	dilation T/F
nf	integer, number of kept axes
lw	row weights, a vector with I components
cw	column weights, a vector with K components
row.coor	partial row coordinates
col.coor	partial column coordinates
row.rel	quality of the representation of the partial rows
col.rel	quality of the representation of the partial columns
row.cwit	contribution to the intra inertia of the partial rows
row.cwitS	contribution to the intra inertia of the partial rows on the S subspace
col.cwit	contribution to the intra inertia of the partial columns
col.cwitS	contribution to the intra inertia of the partial columns on the S subspace
row.wit	intra inertia of the rows
row.witS	intra inertia of the rows on the S subspace
col.wit	intra inertia of the columns
col.witS	intra inertia of the columns on the S subspace
quaj	quality of the representation of the partial row clouds (J)
qual	quality of the representation of the partial colum clouds (L)
betj	intra inertia/total inertia of the whole partial row clouds on the axis
betjS	intra inertia/total inertia of the whole partial row clouds on the S subspace
betl	intra inertia/total inertia of the whole partial column clouds on the axis
betlS	intra inertia/total inertia of the whole partial column clouds on the S subspace
inLJ	cotribution to the inertia of blocks (l,j)
cancorj	cannonical correlation of the partial row clouds
cancorl	cannonical correlation of the partial column clouds

parwwm	an object of class parwwm
x	an object of class parwwm

value

list: rbl,cbl,nr,nc

Author(s)

Campo Elías PARDO [e-mail]

References

Becue M., Pagès 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

Pages, J. (2004) Multiple Factor Analysis: Main Features and Application to Sensory Data. Revista Colombiana de Estadística. 27(1) 1–26

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$tab))),sep="")
coa <- dudi.coa(ardeche$tab, scann = FALSE, nf = 4)
ww <- witwit.model(coa, ardeche$row.blocks, ardeche$col.blocks, scannf = FALSE)
parica <- partial.wwm(ww)
parica

Example output

Loading required package: ade4
Loading required package: xtable
Loading required package: FactoClass
Loading required package: ggplot2
Loading required package: ggrepel
Loading required package: scatterplot3d

 Coordinates were amplified by weight cloud inverses 
Partial coordinates on witwit.modelclass: parwwm
$call: partial.wwm(ACww = ww)

 Partial coordinates are dilated by number of bands 

$nf: 2 axis-components saved   Subespace S = R^ 2 

$betjS: 35.58152 band-column global quality on S
$betlS: 52.69104 band-row global quality on S 

  vector     length mode    content                           
1 $cw        35     numeric column weights                    
2 $lw        43     numeric row weights                       
3 $betl      2      numeric band-row global quality           
4 $betj      2      numeric band-column global quality        
5 $row.witS  43     numeric row within-inertia on S           
6 $col.witS  35     numeric column within-inertia on S        
7 $row.cwitS 258    numeric partial row within-inertia on S   
8 $col.cwitS 140    numeric partial column within-inertia on S

   data.frame nrow ncol content                            
1  $row.coor  258  2    partial row coordinates            
2  $col.coor  140  2    partial column coordinates         
3  $row.rel   258  2    partial row quality                
4  $col.rel   140  2    partial column quality             
5  $row.wit   43   2    row within-inertia                 
6  $col.wit   35   2    column within-inertia              
7  $row.cwit  258  2    partial-row within-inertia         
8  $col.cwit  140  2    partial-column within-inertia      
9  $qual      4    2    representtion quality cloud l      
10 $quaj      6    2    representtion quality cloud j      
11 $inLJ      4    6    inertia blocks (l,j)               
12 $ji        258  2    partial row indicators             
13 $lk        140  2    partial column indicators          
14 $cancorj   12   2    band-column cannonical correlations
15 $cancorl   12   2    row-column cannonical correlations 

pamctdp documentation built on May 1, 2019, 10:19 p.m.