# fast: Factorial Approach for Sorting Task data In SensoMineR: Sensory data analysis with R

## Description

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.

## Usage

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

## Arguments

 `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 `val` boolean, if TRUE elements of validity are calculating (it is time consuming) `B.val` the number of simulations used to obtain the elements of validity `label.miss` label associated with missing groups in the case of incomplete data set

## Value

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 `validity` the elements of validity calculated for the first eigenvalue and the ellipses `call` a list with some statistics

## Author(s)

Marine Cadoret, S\'ebastien L\^e [email protected]

## References

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

## Examples

 ```1 2 3 4 5 6``` ```## Not run: data(perfume) ## Example of FAST results res.fast<-fast(perfume) ## End(Not run) ```

SensoMineR documentation built on May 31, 2017, 4:23 a.m.