Multivariate Methods • Partial Least Squares Models
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Partial Least Squares Models
Develop Models Using Correlations between Ys and Xs
The Partial Least Squares (PLS) platform fits linear models based on factors, namely, linear combinations of the explanatory variables (Xs). These factors are obtained in a way that attempts to maximize the covariance between the Xs and the response or responses (Ys). PLS exploits the correlations between the Xs and the Ys to reveal underlying latent structures.
JMP Pro provides additional functionality, allowing you to conduct PLS Discriminant Analysis (PLS-DA), include a variety of model effects, utilize several validation methods, impute missing data, and obtain bootstrap estimates of the distributions of various statistics.
Partial least squares performs well in situations such as the following, where the use of ordinary least squares does not produce satisfactory results: More X variables than observations; highly correlated X variables; a large number of X variables; several Y variables and many X variables.
A Portion of a Partial Least Squares Report
Contents 
Overview of the Partial Least Squares Platform
Example of Partial Least Squares
Launch the Partial Least Squares Platform
Model Launch Control Panel
Partial Least Squares Report
Model Comparison Summary
<Cross Validation Method> and Method = <Method Specification>
Model Fit Report
Partial Least Squares Options
Model Fit Options
Variable Importance Plot
VIP vs Coefficients Plots
Save Columns
Statistical Details
Partial Least Squares
van der Voet T2
T2 Plot
Confidence Ellipses for X Score Scatterplot Matrix
Standard Error of Prediction and Confidence Limits
Standardized Scores and Loadings
PLS Discriminant Analysis (PLS-DA)