Preference Mapping Techniques

Description

Performs preference mapping techniques based on multidimensional exploratory data analysis.

Usage

1
2
3
4
5
6
carto(Mat, MatH, 
      level = 0, regmod = 1, coord = c(1, 2), asp = 1, 
      cex = 1.3, col = "steelblue4", font = 2, clabel = 0.8,
      label.j = FALSE, resolution = 200, nb.clusters = 0,
	    graph.tree=TRUE,graph.corr=TRUE,graph.carto=TRUE,
	    main=NULL,col.min=7.5,col.max=0)

Arguments

Mat

a data frame corresponding to the axes of the map

MatH

a data frame in which each row represent a product and each column represent the hedonic scores of a given consumer for the products

level

the number of standard deviations used in the calculation of the preference response surface for all the consumers

regmod

the type of regression model used in the calculation of the preference response surface for all the consumers. regmod = 1: quadratic model, regmod = 2: vector model, regmod = 3: circular model, regmod = 4: elliptical model

coord

a vector of length 2, the rank of the axis used to display the results if "manual" is not assigned to the option parameter

asp

if 1 is assigned to that parameter, the graphic displays are output in an orthonormal coordinate system

cex

cf. function par in the graphics package

col

cf. function par in the graphics package

font

cf. function par in the graphics package

clabel

cf. the ade4 package

label.j

boolean, if T then the labels of the panelists who gave the hedonic scores are displayed

resolution

resolution of the map

nb.clusters

number of clusters to use (by default, 0 and the optimal numer of clusters is calculated

graph.tree

boolean, if TRUE plots the tree in 2 dimensions

graph.corr

boolean, if TRUE plots the variables factor map

graph.carto

boolean, if TRUE plots the preference map

main

an overall title for the plot

col.min

define the color which match to the low levels of preference

col.max

define the color which match to the high levels of preference

Details

The preference mapping methods are commonly used in the fields of market research and research and development to explore and understand the structure and tendencies of consumer preferences, to link consumer preference information to other data and to predict the behavior of consumers in terms of acceptance of a given product.
This function refers to the method introduced by M. Danzart. A response surface is computed per consumer; then according to certain threshold preference zones are delimited and finally superimposed.

Author(s)

Francois Husson husson@agrocampus-rennes.fr
Sebastien Le Sebastien.Le@agrocampus-rennes.fr

References

Danzart M., Sieffermann J.M., Delarue J. (2004). New developments in preference mapping techniques: finding out a consumer optimal product, its sensory profile and the key sensory attributes. 7th Sensometrics Conference, July 27-30, 2004, Davis, CA.

See Also

MFA, GPA

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
## Example 1: carto for the sensory descriptors
data(cocktail)
res.pca <- PCA(senso.cocktail)
res.carto <- carto(res.pca$ind$coord[,1:2], hedo.cocktail)

## Example 2
## Not run: 
data(cocktail)
res.mfa <- MFA(cbind.data.frame(senso.cocktail,compo.cocktail),
    group=c(ncol(senso.cocktail),ncol(compo.cocktail)),
    name.group=c("senso","compo"))
res.carto <- carto(res.mfa$ind$coord[,1:2], hedo.cocktail)

## End(Not run)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.