# Estimation of interaction coefficients

### Description

Computes automatically the interaction coefficients between two quantitative variables
`col.p`

and `col.j`

for the following model:
`"~col.p+col.j+col.p:col.j"`

.

### Usage

1 |

### Arguments

`donnee` |
a data frame made up of at least two qualitative variables
( |

`col.p` |
the position of the |

`col.j` |
the position of the |

`firstvar` |
the position of the first endogenous variable |

`lastvar` |
the position of the last endogenous variable (by default the last column of |

### Details

In most cases `col.p`

represents the *product* effect, `col.j`

represents the *panelist* effect,
and the variables of interest are the sensory descriptors. The model considered is the following one:
`"~Product+Panelist+Product:Panelist"`

.

Data must be complete (but not necessarily balanced).

### Value

Returns an array of dimension (*p,j,k*), where *p* is the number of products, *j* the number of panelists
and *k* the number of sensory descriptors.
The entries of this array are the interaction coefficients between a panelist and a product for a given descriptor.

For each sensory descriptor, returns a graph where each (panelist,product) interaction coefficient is displayed,
a graph where the contribution to the (panelist,product) interaction coefficient by product is displayed,
a graph where the contribution to the (panelist,product) interaction coefficient by panelist is displayed.

### Author(s)

Fran<e7>ois Husson

### See Also

`aov`

### Examples

1 2 3 4 5 | ```
## Not run:
data(chocolates)
resinteract=interact(sensochoc, col.p = 4, col.j = 1, firstvar = 5)
## End(Not run)
``` |