SensoMineR (version 1.20)

IdMapConsumer: Ideal Mapping by categories (IdMapConsumer)

Description

Create the ideal map and plot the ideal areas of the categories of qualitative variables. And perform 2 tests: a global test in order to highlight the significance of the difference between ideals of all the categories of the same variable; a pair comparison test to highlight the significance between 2 categories of the same variable.

Usage

IdMapConsumer(dataset.id, dataset.signa, col.p, col.j, col.lik, 
	num.col.var.signa, conf.level=0.95, id.recogn, nbchoix = NULL, nbsimul = 500, alpha = 0.05, coord = c(1, 2), precision = 0.1, levels.contour = NULL, color = FALSE, simusigni = 500)

Arguments

dataset.id

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)

dataset.signa

a data frame with n rows (individuals) and p columns (categorical variables)

col.p

The position of the product variable in the dataframe dataset.id

col.j

The position of the consumer variable in the dataframe dataset.id

col.lik

The position of the liking variable in the dataframe dataset.id

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.

num.col.var.signa

The position of the categorical variables in the dataframe dataset.signa you want to plot the ideal area of the different modalities/you want to know if the ideal product of the different modalities is significantly different

conf.level

Threshold used for the tests

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

alpha

The confidence level of the ellipses

coord

A length 2 vector specifying the components to plot

precision

The value defining the step when gridding the space

levels.contour

The levels (between 0 and 1) to consider for the colors on the surface plot. By default, they are set automatically based on the results

color

Boolean, define whether the map is in color or in black and white

simusigni

The number of simulations used to perform the global and the pair comparison test

Value

A list containing the following components:

PCA

the results from the PCA used to create the sensory space

idmap

a list containing the results of the IdMap (data), the weight for each consumer (j.weight) and the precision used.

ideal

a list containing the estimated profile of the ideal of reference (not available for the wIdMap) as well as the percentage of consumers concerned

coordobs

The coordinates of all the ideals of all the categories on the sensory space

test.global

The results for the global test for each variables (observed inertia, critical inertia, P-value)

test.paires

The results for the pair comparison test for each variables, between its ideal's categories(observed distance between two categories, critical distance, P-value)

The three last components are provided only if the user choose "color = FALSE", else no test and no ideal map with categories' ideal are performed.

Details

The IdMapConsumer, step by step: Step 1: the classical IdMap is plotted with the method "ellipses" Step 2: for each modality of the categorical variable, the optimum of the ideal area is calculated with the method "density" Step 3: for each categorical variable given in num.col.var.signa, simulations are performed giving the p-value for the global ant the pair comparison test. Step 4: if the global test is significant for a variable, the ideal areas of its modalities are plotted on the IdMap

This function needs the KernSmooth package.

References

Worch, T., Le, S., Punter, P., & Pages, J. (2012). Construction of an Ideal Map (IdMap) based on the ideal profiles obtained directly from consumers. Food Quality and Preference, 26, 93-104.

See Also

IdMap

Examples

## Not run: 
# ###Load the two datasets
# data(cream_id)
# data(cream_signa)
# 
# ###Run the analysis and test the ideals of the variables from 1 to 12 
# ## for example with a confidence level of 90
# res.idmap <- IdMapConsumer(cream_id, cream_signa, col.p=2, col.j=1, col.lik=29, 
#    num.col.var.signa=c(1:12),conf.level=0.90,id.recogn="id_") 
# ## End(Not run)