Description Usage Arguments Details Value Author(s) See Also Examples
Just About Right
1 | JAR(x, col.p, col.j, col.pref, jarlevel="jar")
|
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) |
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
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
Francois Husson
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