SensoMineR (version 1.27)

indscal: Construct the Indscal model for Napping data type

Description

This version of the Indscal model is specially adapted to Napping data type, i.e. products (stimuli) are positioned on a tableclothe by panelists, then their coordinates are used as input for the Indscal model.

Usage

indscal(matrice, matrice.illu = NULL, maxit = 200, coord = c(1,2), 
    eps = 1/10^5)

Value

Returns a list including:

W

a matrix with the subject coordinates

points

a matrix with the stimuli (individuals) coordinates

subvar

a vector with the strain between each configuration and the stimuli configuration

r2

the strain criterion

The functions returns the three following graphs:

A stimuli representation, ie. a representation of the products

A representation of the weights computed by the Indscal model.

A correlation circle of the variables enhanced by illustrative variables (supplementary columns)

Arguments

matrice

a data frame of dimension (p,2j), where p represents the number of products and j the number of panelists (two coordinates per panelist)

matrice.illu

a data frame with illustrative variables (with the same row.names in common as in matrice)

maxit

the maximum number of iterations until the algorithm stops

coord

a length 2 vector specifying the components to plot

eps

a threshold with respect to which the algorithm stops, i.e. when the difference between the criterion function at step n and n+1 is less than eps

Author

Peter Ellis
Francois Husson

References

Carroll, J.D. & J.J. Chang (1970). Analysis of individual differences in multidimensional scaling via an N-way generalization of "Eckart-Young" decomposition. Psychometrika, 35, 283-319.

See Also

nappeplot, pmfa

Examples

if (FALSE) {
data(napping)
nappeplot(napping.don)
resindscal<- indscal(napping.don, napping.words)
prefpls(cbind(resindscal$points, napping.words))
pmfa(napping.don, napping.words, mean.conf = resindscal$points)
}