MultiIdeal function

Single vs. Multiple Ideal

By the use of confidence ellipses, this procedure checks whether consumers associate the different products tested to a single or to multiple ideals.

MultiIdeal(dataset, col.p, col.j, id.recogn, level.search.desc=0.2, correct=FALSE,
            nbchoix=NULL, nbsimul=500, coord=c(1,2))

Arguments

• dataset: A matrix with at least two qualitative variables (consumer and products) and a set of quantitative variables containing at least 2*A variables (for both perceived and ideal intensities)

• col.p: The position of the product variable

• col.j: The position of the consumer variable

• id.recogn: The sequence in the variable names which distinguish the ideal variables from the sensory variables. This sequence should be fixed and unique.

  Each ideal variable should be preceeded by the corresponding perceived intensity variable.

• level.search.desc: the threshold above which a descriptor is not considered as discriminant according to AOV model "descriptor=Product+Panelist".

• correct: Boolean, define whether the ideal products should be corrected from the difference in the use of the scale or not

• nbchoix: The number of consumers forming a virtual panel, by default the number of panelists in the original panel

• nbsimul: The number of simulations (corresponding to the number of virtual panels) used to compute the ellipses

• coord: A length 2 vector specifying the components to plot

Details

The procedure of MultiIdeal, step by step:

Step 1: the sensory and ideal variables are separated into two tables.

Step 2: the product space is created by PCA on the averaged sensory table (averaged by product).

Step 3: the ideal information (Product x Consumer) is projected as supplementary entities in this space.

Step 4: confidence ellipses are created around the averaged ideal points associated to each product (using the consumer variability).

Returns

Returns a matrix with the P-values of the Hotelling's T2 tests for each pair of products.

References

Worch, T., & Ennis, J.M. (2013). Investigating the single ideal assumption using Ideal Profile Method. Food Quality and Preference.

Author(s)

Worch Thierry (thierry@qistatistics.co.uk)

See Also

panellipse

Examples

## Not run:

data(perfume_ideal)
res <- MultiIdeal(perfume_ideal, col.p=2, col.j=1, id.recogn="id_", 
    level.search.desc=0.2, nbsimul=500, coord=c(1,2))

# To run the analysis with all the attributes
res <- MultiIdeal(perfume_ideal, col.p=2, col.j=1, id.recogn="id_", 
    level.search.desc=1, nbsimul=500, coord=c(1,2))
## End(Not run)