JAR: Free choice profiling

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/JAR.R

Description

Free choice profiling with confidence ellipses

Usage

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

Arguments

x

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)

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

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

Author(s)

Francois Husson

See Also

plot.JAR

Examples

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## 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)
 
## End(Not run)

SensoMineR documentation built on Dec. 13, 2017, 9:04 a.m.