ConsistencyIdeal: Sensory and Hedonic consistency of the ideal data

Description

Evaluate the sensory and hedonic consistency of the ideal data, both at the consumer and panel level.

Usage

ConsistencyIdeal(dataset, col.p, col.j, col.lik, id.recogn, type="both", scale.unit=TRUE,
                  ncp=NULL, axes=c(1,2), nbsim=0, replace.na=FALSE, graph=TRUE)

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

col.lik

The position of the liking 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.

type

Define whether you want the sensory consistency only ("sensory"), the hedonic consistency only ("hedonic"), or both ("both")

scale.unit

Boolean, if TRUE the descriptors are scaled to unit variance

ncp

Number of dimensions kept in the results

axes

A length 2 vector specifying the components to plot

nbsim

The number of simulations performed. By default (=0), no simulations are performed and only the results for the real data are given

replace.na

Boolean, define whether the missing values (in the correlation matrix calculated for the consistency at the consumer level) should be ignored or replaced by 0

graph

Boolean, define whether the distribution of the correlation coefficient should be plot

Value

A list containing the results for the sensory and hedonic consistency:
Sensocontains the results of the sensory consistency
Senso$panelresults for the consistency at the panel level including:
Senso$panel$datasetthe datasets used for the different PCA
Senso$panel$PCA.idealthe results of the PCA for the creation of the ideal space
Senso$panel$PCA.ideal_hedothe results of the PCA with projection of the hedonic scores
Senso$panel$PCA.ideal_sensothe results of the PCA with the proojection of the sensory descriptions
Senso$panel$correlationthe correlation between the product projected from the sensory and hedonic points of view
Senso$consoresults of the consistency at the consumer level including:
Senso$conso$driver.likthe linear drivers of liking (correlation between perceived intensity and liking score for each attribute)
Senso$conso$correlationsthe correlations between drivers of liking and the difference (ideal-perceived) intensity
Hedo$R2the R2 coefficients of the indivvidual models
Hedo$hedoa list containing the hedonic scores for the product, ideal products, average ideal product and the standardized ideal product for each consumer
Hedo$simulationa list including the estimated hedonic score for each simulation, the p-value and the matrix of simulations used

Details

SENSORY CONSISTENCY A the panel level: A PCA is performed on the table crossing the J consumers in rows and the A ideal variables in columns (the averaged or corrected averaged is then considered). On this space, the sensory description of the P products (P rows) on the A attributes is projected as supplementary entities while the hedonic table crossing the J consumers (in rows) and the P products (in columns) is projected as supplementary variables. The sensory consistency is measured by the correspondence between the same products seen through the sensory and through the hedonic descriptions. At the consumer level: For each consumer, the correlation between the (corrected) ideal ratings and the correlation between the hedonic scores and the perceived intensity of each attribute is calculated. A test on this correlation coefficient is performed for each consumer. The distribution of these correlations coefficients are also given graphically. HEDONIC CONSISTENCY For each consumer, a PCR-model expressing the liking scores in function of the perceived intensity is created. Once the model is created, the model is applied to the ideal ratings provided by the consumer considered and the hedonic score of the ideal product is estimated. This hedonic score is then compared to the hedonic scores provided to the products tested. If simulations are asked, the same procedure is estimated after re-sampling the vector of hedonic scores. In that case, the distribution of the estimated ideal hedonic score can be estimated under H0 and the significance of the estimated ideal hedonic score can be done.

References

{Worch, T., Le, S., Punter, P., & Pages, J. (2012). Assessment of the consistency of ideal profiles according to non-ideal data for IPM. Food Quality and Preference, 24, 99-110.}, {Worch, T., Le, S., Punter, P., & Pages, J. (2012). Extension of the consistency of the data obtained with the Ideal Profile Method: Would the ideal products be more liked than the tested products? Food Quality and Preference, 26, 74-80.}

Examples

Run this code

data(perfume_ideal)
res <- ConsistencyIdeal(perfume_ideal, col.p=2, col.j=1, 
   col.lik=ncol(perfume_ideal), id.recogn="id_", 
   type="both", nbsim=100)

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