SensoMineR (version 1.0)

carto: Preference Mapping Techniques

Description

Performs preference mapping techniques based on multidimensional exploratory data analysis.

Usage

carto(Mat, MatH, option = c("pca", "manual","afmult", "statis", "gpa"), level = 0, regmod = 1, graphic = FALSE, coord = c(1, 2), asp = 1, cex = 1.3, col = "steelblue4", font = 2, clabel = 0.8, label.j = FALSE,resolution=200,nb.clusters=0)

Arguments

Mat

a data frame or an object of class ktab (i.e. a list of data frames with the same row.names in common). If "pca" or "manual" are assigned to the option parameter, then Mat must be a data frame, otherwise Mat is an object of cla

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

option

the multidimensional exploratory data analysis to be performed, e.g. Principal Components Analysis ("pca"), Multiple Factor Analysis ("afmult"), Statis method ("statis"), Generalised Procrustes Analysis ("g

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

graphic

if TRUE is assigned to that parameter, each graphic displayed is output in a specific window

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

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.

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

afmult, gpa

Examples

## Example 1
data(chocolates)
data(hedochoc)
chocselect <- search.desc(chocolates,col.j=1,col.p=4,firstvar=5)
resaveragetable<-averagetable(chocselect,formul="~Product",firstvar=5)
carto(resaveragetable, hedochoc, option = "pca")

## Example 2
data(sensopanels)
data(hedochoc)
ktabsenso <- ktab.data.frame(sensopanels, blocks = rep(14,7))
carto(ktabsenso,hedochoc,option = "afmult", graphic = TRUE)