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Select Analyze > Multivariate Methods > Partial Least Squares.

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Select Analyze > Fit Model and select Partial Least Squares from the Personality menu. This approach enables you to do the following:

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In JMP Pro, you can enter nominal response columns in the Fit Model launch window to conduct PLSDA. For details, see PLS Discriminant Analysis (PLSDA).
Enter the predictor columns. The Partial Least Squares launch window only allows numeric predictors.
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Centers all Y variables and model effects by subtracting the mean from each column. See Centering and Scaling.
Scales all Y variables and model effects by dividing each column by its standard deviation. See Centering and Scaling.
(Fit Model launch window only) Select this option to center and scale all columns that are used in the construction of model effects. If this option is not selected, higherorder effects are constructed using the original data table columns. Then each higherorder effect is centered or scaled, based on the selected Centering and Scaling options. Note that Standardize X does not center or scale Y variables. See Standardize X.
Replaces missing data values in Ys or Xs with nonmissing values. Select the appropriate method from the Imputation Method list.
If Impute Missing Data is not selected, rows that are missing observations on any X variable are excluded from the analysis and no predictions are computed for these rows. Rows with no missing observations on X variables but with missing observations on Y variables are also excluded from the analysis, but predictions are computed.
(Appears only when Impute Missing Data is selected) Select from the following imputation methods:
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Mean: For each model effect or response column, replaces the missing value with the mean of the nonmissing values.

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EM: Uses an iterative ExpectationMaximization (EM) approach to impute missing values. On the first iteration, the specified model is fit to the data with missing values for an effect or response replaced by their means. Predicted values from the model for Y and the model for X are used to impute the missing values. For subsequent iterations, the missing values are replaced by their predicted values, given the conditional distribution using the current estimates.

After completing the launch window and clicking OK, the Model Launch control panel appears. See Model Launch Control Panel.