indscal: Construct the Indscal model for Napping data type
In SensoMineR: Sensory data analysis with R

Description Usage Arguments Value Author(s) References See Also Examples

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.

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

`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`

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)

Peter Ellis
Fran<e7>ois Husson

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.

nappeplot, pmfa

## Not run: 
data(napping)
nappeplot(napping.don)
resindscal<- indscal(napping.don, napping.words)
x11()
prefpls(cbind(resindscal$points, napping.words))
x11()
pmfa(napping.don, napping.words, mean.conf = resindscal$points)

## End(Not run)