If you don't need to include a lot of reference or operational layers and you don't need to apply specific styles to the map's content, use a URL to open Map Viewer.On the other hand, the regions with hot colors (red) indicate a high proportion of models with high preferences.For example, you can use a Map Viewer URL in the following circumstances: ![]() ![]() In the regions with cold colors (blue), a low proportion of models give high preferences. At each point on the chart, the percentage of judges for whom the preference calculated from the model is greater than their mean preference is calculated. The contour plot shows the regions corresponding to the various preference consensus levels on a chart whose axes are the same as the preference map. A preference order of objects is deducted from the preference scores for each of the judges. The more the product is preferred, the higher the score. The preference score for each object for a given judge, whose value is between 0 (minimum) and 1 (maximum), is calculated from the prediction of the model for the judge. However, the models associated with the judges must be adjusted correctly in order that the interpretation is reliable. The PREFMAP, with the interpretation given by the preference map is an aid to interpretation and decision-making which is potentially very powerful since it allows preference data to be linked to objective data. The preference map is a summary view of three types of elements: The judges (or groups of judges if a classification of judges has been carried out beforehand) represented in the corresponding model by a vector, an ideal point (labeled +), an anti-ideal point (labeled -), or a saddle point (labeled o) The objects whose position on the map is determined by their coordinates The descriptors which correspond to the representation axes with which they are associated (when a PCA precedes the PREFMAP, a biplot from the PCA is studied to interpret the position of the objects as a function of the objective criteria). XLSTAT displays detailed results in addition to the preference map to facilitate the interpreting of results. XLSTAT offers several regression models to project complementary data on the objects maps: Preference mapping provides a powerful approach to optimizing product acceptability.
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