lignin sulfonate (ls), which is pulp industry pollution
humic acid (ha), which is a natural forest product
Select Help > Sample Data Library and open
Note: The data in the data table are reported in Umetrics (1995). The original source is Lindberg, Persson, and Wold (1983).
Select Analyze > Multivariate Methods > Partial Least Squares.
Assign ls, ha, and dt to the Y, Response role.
Assign Intensities, which contains the 27 intensity variables v1 through v27, to the X, Factor role.
Select Leave-One-Out as the Validation Method.
A portion of the report appears in Partial Least Squares Report. Since the van der Voet test is a randomization test, your Prob > van der Voet T2 values can differ slightly from those in Partial Least Squares Report.
Partial Least Squares Report
The van der Voet T2 statistic tests to determine whether a model with a different number of factors differs significantly from the model with the minimum PRESS value. A common practice is to extract the smallest number of factors for which the van der Voet significance level exceeds 0.10 (SAS Institute Inc, 2011 and Tobias, 1995). If you were to apply this thinking here, you would fit a new model by entering 6 as the Number of Factors in the Model Launch panel.
Seven Extracted Factors
Select Diagnostics Plots from the NIPALS Fit with 7 Factors red triangle menu.
Diagnostics Plots
Select VIP vs Coefficients Plot from the NIPALS Fit with 7 Factors red triangle menu.
VIP vs Coefficients Plot
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)
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:
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.
All model effects are then centered or scaled, in accordance with your selections of the Centering and Scaling options, prior to inclusion in the model.
If the Standardize X option is not selected, and Centering and Scaling are both selected, then the term that is entered into the model is calculated as follows: