# 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

1 2 |

### Arguments

`matrice` |
a data frame of dimension ( |

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

`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 |

### 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)

### Author(s)

Peter Ellis

Fran<e7>ois 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

1 2 3 4 5 6 7 8 9 10 | ```
## 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)
``` |