Ideal Mapping (IdMap)
Create the ideal map, a map based on the ideal profiles provided by the consumers.
IdMap(dataset, col.p, col.j, col.lik, id.recogn, nbsimul=500, nbchoix=NULL, alpha=0.05, coord=c(1,2), precision=0.1, levels.contour=NULL, color=FALSE, cons.eq=FALSE)
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 preceded by the corresponding perceived intensity variable.
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
cons.eq: Boolean, define whether the IdMap (by default) or the wIdMap is performed
The IdMap, 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 averaged ideal product of each consumer is projected as supplementary entities in this space.
Step 4: confidence ellipses are created around each individual averaged ideal product using truncated total bootstrap.
Step 5: for each consumer, the space is grid and the position where the ideal area is defined is marked: individual surfaces of response are created.
Step 6: (optional) the ellipses can be balanced by applying individual weight (all the ellipses have a weigth of 1, however the size of the ellipse). wIdMap is then performed.
Step 7: all the individual surface plots are added together and a surface plot is created.
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
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.
Worch Thierry (thierry@qistatistics.co.uk)
plot.IdMap, carto, boot
## Not run: data(perfume_ideal) #! For the IdMap res.IdMap <- IdMap(perfume_ideal, col.p=2, col.j=1, col.lik=ncol(perfume_ideal), id.recogn="id_") plot.IdMap(res.IdMap, xlim=c(-7,10), ylim=c(-5,7), levels.contour=NULL, color=TRUE) #! For the wIdMap res.wIdMap <- IdMap(perfume_ideal, col.p=2, col.j=1, col.lik=ncol(perfume_ideal), id.recogn="id_", cons.eq=TRUE) ## End(Not run)