fast: Factorial Approach for Sorting Task data
In SensoMineR: Sensory Data Analysis

Description Usage Arguments Value Author(s) References Examples

Perform Factorial Approach for Sorting Task data (FAST) on a table where the rows (i) are products and the columns (j) are consumers. A cell (i,j) corresponds either to the number of the group to which the product i belongs for the consumer j, or, in the case of "qualified" categorization, to the sequence of words associted with the group to which the product i belongs for the consumer j.

1 2	fast(don,alpha=0.05,sep.words=" ",word.min=5,graph=TRUE,axes=c(1,2), ncp=5,B=200,label.miss=NULL,ncp.boot=NULL)

`don`	a data frame with n rows (products) and p columns (assesor : categorical variables)
`alpha`	the confidence level of the ellipses
`sep.words`	the word separator character in the case of qualified categorization
`word.min`	minimum sample size for the word selection in textual analysis
`graph`	boolean, if TRUE a graph is displayed
`axes`	a length 2 vector specifying the components to plot
`ncp`	number of dimensions kept in the results (by default 5)
`B`	the number of simulations (corresponding to the number of virtual panels) used to compute the ellipses
`label.miss`	label associated with missing groups in the case of incomplete data set
`ncp.boot`	number of dimensions used for the Procrustean rotations to build confidence ellipses (by default NULL and the number of components is estimated)

A list containing the following elements:

`eig`	a matrix containing all the eigenvalues, the percentage of variance and the cumulative percentage of variance
`var`	a list of matrices containing all the results for the categories (coordinates, square cosine, contributions, v.test)
`ind`	a list of matrices containing all the results for the products (coordinates, square cosine, contributions)
`group`	a list of matrices containing all the results for consumers (coordinates, square cosine, contributions)
`acm`	all the results of the MCA
`cooccur`	the reordered co-occurrence matrix among products
`reord`	the reordered matrix products*consumers
`cramer`	the Cramer's V matrix between all the consumers
`textual`	the results of the textual analysis for the products
`call`	a list with some statistics

Marine Cadoret, S\'ebastien L\^e sebastien.le@agrocampus-ouest.fr

Cadoret, M., L\^e, S., Pag\'es, J. (2008) A novel Factorial Approach for analysing Sorting Task data. 9th Sensometrics meeting. St Catharines, Canada
Cadoret, M., L\^e, S., Pag\'es, J. (2009) A Factorial Approach for Sorting Task data (FAST). Food Quality and Preference. 20. pp. 410-417
Cadoret, M., L\^e, S., Pag\'es, J. (2009) Missing values in categorization. Applied Stochastic Models and Data Analysis (ASMDA). Vilnius, Lithuania

## Not run: 
data(perfume)
## Example of FAST results
res.fast<-fast(perfume,sep.words=";")
res.consensual<-ConsensualWords(res.fast)

## End(Not run)

Loading required package: FactoMineR
dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
dev.new(): using pdf(file="Rplots3.pdf")
dev.new(): using pdf(file="Rplots4.pdf")
dev.new(): using pdf(file="Rplots5.pdf")
dev.new(): using pdf(file="Rplots6.pdf")
dev.new(): using pdf(file="Rplots7.pdf")
Number of different words :  83 
dev.new(): using pdf(file="Rplots8.pdf")
dev.new(): using pdf(file="Rplots9.pdf")
dev.new(): using pdf(file="Rplots10.pdf")