![]() We notice that two observations (3,5) have been reclassified. Each observation is classified into the group for the which the function is the highest. The next table summarizes the classification process. ![]() Once the classical PLS regression outputs have been displayed, the specific PLS discriminant analysis outputs are displayed. We can visualize the species and the explanatory variables in that correlation plot. The first correlations map allows to visualize on the first two components the correlations between the Xs and the components, and the Ys and the components. This indicates that the 4 components generated by the Partial Least Squares regression summarize well both the Xs and the Ys. The cumulated R²Y and R²X cum that correspond to the correlations between the explanatory (X) and dependent (Y) variables with the components are very close to 1 with 4 components. This suggests that the quality of the fit varies a lot depending on the specie. We see that Q² remains low even with 4 components (ideally it should be close to 1). XLSTAT has automatically selected 4 components. The Q² cumulated index measures the global goodness of fit and the predictive quality of the 3 models (one for each specie). The first results displayed are the classical PLS regression results between the explanatory variables and the species (each specie representing one response variable).Īfter the tables displaying the basic statistics and the correlations between all the selected variables (dependent variables are displayed in blue and quantitative explanatory variables in black), the results specific to the PLS regression are presented. Interpreting the results of a Discriminant Analysis The computations begin once you have clicked on OK. Last, in the Charts tab, the Colored labels option has been activated in order to make the reading of the charts easier. In the Options tab of the dialog box, make sure that Automatic is activated. The method to be used is PLS-DA for Partial Least Squares Discriminant Analysis. ![]() In Quantitative variable(s) field, select the explanatory variables, that are in our case the physical descriptors of the iris. ![]() In the Dependent variable(s) field, select with the mouse the species. Once you have clicked the button, the Partial Least Squares regression dialog box is displayed. Start XLSTAT, then select the XLSTAT / Modeling data / Partial Least Squares Regression command in the Excel menu or click the corresponding button on the Modeling data menu. To set up a Partial Least Squares discriminant analysis, you have to use the Partial Least Squares regression dialog box. Setting up a Partial Least Squares discriminant analysis It is based on the Partial Least Squares method and allows to treat multicollinear data, missing values and data set with few observations and many variables. Our goal is to test if the four variables allow to discriminate the species, and to visualize the observations on a 2-dimensional map that shows as well as possible how separated the groups are. Goal of the Partial Least Squares discriminant analysis in this example Three different species have been included in this study: setosa, versicolor and virginica. The data are from and correspond to 150 Iris flowers, described by four variables (sepal length, sepal width, petal length, petal width) and their species. Dataset for running a Partial Least Squares discriminant analysis This tutorial shows how to set up and interpret a Partial Least Squares Discriminant Analysis in Excel using the XLSTAT software.
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