fast function

Factorial Approach for Sorting Task data

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.

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)

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
• 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)

Returns

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

References

Cadoret, M., Le, S., Pages, J. (2008) A novel Factorial Approach for analysing Sorting Task data. 9th Sensometrics meeting. St Catharines, Canada

Cadoret, M., Le, S., Pages, J. (2009) A Factorial Approach for Sorting Task data (FAST). Food Quality and Preference. 20. pp. 410-417

Cadoret, M., Le, S., Pages, J. (2009) Missing values in categorization. Applied Stochastic Models and Data Analysis (ASMDA). Vilnius, Lithuania

Author(s)

Marine Cadoret, Sebastien Le sebastien.le@institut-agro.fr

Examples

## Not run:

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