SensoMineR (version 1.27)

decat: DEscription of CATegories

Description

This function is designed to point out the variables that are the most characteristic according to the set of products in its whole, and to each of the products in particular.

This function is designed to test the main effect of a categorical variable (F-test) and the significance of its coefficients (T-test) for a set of endogenous variables and a given analysis of variance model. In most cases, the main effect is the product effect and the endogenous variables are the sensory descriptors.

Usage

decat(donnee, formul, firstvar, lastvar = length(colnames(donnee)), 
    proba = 0.05, graph = TRUE, col.lower = "mistyrose", 
    col.upper = "lightblue", nbrow = NULL, nbcol = NULL, random = TRUE)

Value

A list containing the following elements:

tabF

the V-test and the P-value of the F-test for each descriptor resulting from the analysis of variance model

tabT

a (products,descriptors) data frame, in which each cell is the Vtest for a given product and a given descriptor

coeff

a (products,descriptors) data frame, in which each cell is the coefficient resulting from the analysis of variance model for a given product and a given descriptor

resF

the V-test and the P-value for each descriptor resulting from the analysis of variance model, sorted in ascending order

resT

a list which elements are data frames, one data frame per product: the coefficient, the P-value and the Vtest for each significant descriptor resulting from the analysis of variance model, sorted in descending order

adjmean

a (products,descriptors) data frame, in which each cell is the adjusted mean resulting from the analysis of variance model for a given product and a given descriptor

A barplot of the P-values associated with the F-test of the product effet.

A colored table with the adjusted means of the categorical variable: the values significantly different from the general mean are colored (significantly different with the proba level); the significantly less are colored in red (by default) and the significantly great are colored in blue.

Arguments

donnee

a data frame made up of at least two qualitative variables (product, panelist) and a set of quantitative variables (sensory descriptors)

formul

the model that is to be tested

firstvar

the position of the first endogenous variable

lastvar

the position of the last endogenous variable (by default the last column of donnee)

proba

the significance threshold considered for the analyses of variance (by default 0.05)

graph

a boolean, if TRUE a barplot of the P-values associated with the F-test of the product effet is displayed

col.lower

the color used for 'level.lower'. Only useful if graph is TRUE

col.upper

the color used for 'upper.lower'. Only useful if graph is TRUE

nbrow

the number of rows to be displayed (by default, all the values are displayed). Only useful if graph is TRUE

nbcol

the number of columns to be displayed (by default, all the values are displayed). Only useful if graph is TRUE

random

boolean, effect should be possible as fixed or random (default as random)

Author

Francois Husson

Details

The formul parameter must be filled in by an analysis of variance model and must begin with the categorical variable of interest (e.g. the product effect) followed by the different other factors of interest (and their combinations). E.g.:formul = "~Product+Panelist+Session".

References

P. Lea, T. Naes, M. Rodbotten. Analysis of variance for sensory data.
H. Sahai, M. I. Ageel. The analysis of variance.

See Also

aov

Examples

### Example 1
data(chocolates)
## model (AOV): " descriptor = product + panelist "
resdecat<-decat(sensochoc, formul="~Product+Panelist", firstvar = 5)
barrow(resdecat$tabT)
barrow(t(resdecat$tabT), numr = 3, numc = 3)
barrow(resdecat$coeff, color = "orange") 

### Example 2
data(chocolates)
## model (AOV): " descriptor = product + panelist "
res2 <-decat(sensochoc, formul="~Product+Panelist", firstvar = 5,
    proba=1, graph = FALSE)