fahst: Factorial Approach for Hierarchical Sorting Task data

Description Usage Arguments Value Author(s) References Examples

View source: R/fahst.r

Description

Perform Factorial Approach for Hierarchical Sorting Task data (FAHST) on a table where the rows (i) are products and the columns (j) are for each consumer the partitionning variables associated with nested sorting. The columns are grouped by consumer. For the partitionning variables, the label associated with a group can be an arbirary label (for example G1 for group 1, etc.) or the words associated with the group in the case of qualified hierarchical sorting.

Usage

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fahst(don,group,alpha=0.05,graph=TRUE,axes=c(1,2),name.group=NULL,ncp=5,B=200,ncp.boot=2)

Arguments

don

a data frame with n rows (products) and p columns (nested partitions for all consumers)

group

a list indicating the number of levels (nested partitions) for each consumer

alpha

the confidence level of the ellipses

graph

boolean, if TRUE a graph is displayed

axes

a length 2 vector specifying the components to plot

name.group

a vector containing the name of the consumers (by default, NULL and the consumers are named J1, J2 and so on)

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

ncp.boot

number of dimensions used for the Procrustean rotations to build confidence ellipses (by default 2)

Value

A list containing the following elements:

eig

a matrix containing all the eigenvalues, the percentage of variance and the cumulative percentage of variance

ind

a list of matrices containing all the results for the products (coordinates, square cosine, contributions)

var

a list of matrices containing all the results for the categories of the different nested partitions (coordinates, square cosine, contributions, v.test)

group

a list of matrices containing all the results for consumers (coordinates, square cosine, contributions)

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. (2010) A new approach for analyzing hierarchical sorting task data. Sensometrics conference. Rotterdam, the Netherlands

Examples

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## Not run: 
data(cards)
## Example of FAHST results
group.cards<-c(2,3,3,2,2,4,2,3,2,1,3,2,3,3,3,2,3,3,2,3,3,3,3,3,3,3,3,3,3,3)
res.fahst<-fahst(cards,group=group.cards)

## End(Not run)

SensoMineR documentation built on Dec. 13, 2017, 9:04 a.m.