Select Analyze > Multivariate Methods > Partial Least Squares.
Select Analyze > Fit Model and select Partial Least Squares from the Personality menu. This approach enables you to do the following:
Enter categorical variables as Ys or Xs. Conduct PLS-DA by entering categorical Ys.
JMP Pro Partial Least Squares Launch Window (Imputation Method EM Selected)
Standardize X
Impute Missing Data
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.
Imputation Method
(Appears only when Impute Missing Data is selected) Select from the following imputation methods:
Mean: For each model effect or response column, replaces the missing value with the mean of the nonmissing values.
EM: Uses an iterative Expectation-Maximization (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.
Max Iterations

Help created on 9/19/2017