indscal: Construct the Indscal model for Napping data type
In SensoMineR: Sensory Data Analysis

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
Francois 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)
prefpls(cbind(resindscal$points, napping.words))
pmfa(napping.don, napping.words, mean.conf = resindscal$points)

## End(Not run)

Loading required package: FactoMineR
dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
dev.new(): using pdf(file="Rplots3.pdf")
dev.new(): using pdf(file="Rplots4.pdf")
dev.new(): using pdf(file="Rplots5.pdf")
dev.new(): using pdf(file="Rplots6.pdf")
dev.new(): using pdf(file="Rplots7.pdf")
dev.new(): using pdf(file="Rplots8.pdf")
dev.new(): using pdf(file="Rplots9.pdf")
dev.new(): using pdf(file="Rplots10.pdf")
dev.new(): using pdf(file="Rplots11.pdf")
dev.new(): using pdf(file="Rplots12.pdf")
dev.new(): using pdf(file="Rplots13.pdf")
dev.new(): using pdf(file="Rplots14.pdf")
dev.new(): using pdf(file="Rplots15.pdf")
     RV coeff
Y1  0.7374218
Y2  0.3188305
Y3  0.5362422
Y4  0.6055386
Y5  0.6996772
Y6  0.1973223
Y7  0.6787594
Y8  0.4465223
Y9  0.6995525
Y10 0.6114401
Y11 0.5222591