Factorial Approach for Sorting Task data

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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

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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 sebastien.le@agrocampus-ouest.fr

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

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## Not run: 
data(perfume)
## Example of FAST results
res.fast<-fast(perfume)

## End(Not run)

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