JAR: Free choice profiling

Description

Free choice profiling with confidence ellipses

Usage

JAR(x, col.p, col.j, col.pref, jarlevel="jar")

Arguments

data.frame

col.p

the position of the product variable

col.j

the position of the panelist variable

col.pref

the position of the preference variable

jarlevel

a string corresponding to the jar level (the level must be the same for all the jar variables)

Value

Returns a list of 3 objects. The penalty1 object corresponds to the one-dimensional penalty results: a data-frame with the penalty coefficient in the first column, the standard deviation and the p-value for the test that the penalty is significantly different from 0. The penalty2 object corresponds to the mutli-dimensional penalty results: a data-frame with the penalty coefficient in the first column, the standard deviation and the p-value for the test that the penalty is significantly different from 0. The Frequency object gives the percentage of times the non-jar categories are given for each product: a matrix with the non-jar categories in rows and the products in columns

Details

Perform the penalty analysis. Two models are constructed. The one-dimensional model is constructed descriptor by descriptor. For descriptor_j the model is: Hedonic score = Descriptor_j_Not enough+ Descriptor_j_Too much The multi-dimensional model is constructed with all descriptors simultaneously: Hedonic score = Descriptor_1_Not enough+ Descriptor_1_Too much +...+ Descriptor_p_Not enough+ Descriptor_p_Too much+ Product + Judge

Examples

Run this code

# NOT RUN {
data(JAR)
res.jar <- JAR(JAR,col.p=13,col.j=1,col.pref=2)
plot(res.jar,name.prod="284", model=1)
 
# }