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Multivariate Methods > Partial Least Squares Models
Publication date: 11/10/2021

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.

Image shown hereJMP Pro provides additional functionality, enabling you to conduct PLS Discriminant Analysis (PLS-DA), include a variety of model effects, use 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.

Figure 6.1 A Portion of a Partial Least Squares ReportĀ 

A Portion of a Partial Least Squares Report


Overview of the Partial Least Squares Platform

Example of Partial Least Squares

Launch the Partial Least Squares Platform

Centering and Scaling
Standardize X

Model Launch Control Panel

Partial Least Squares Options

Partial Least Squares Report

Model Comparison Summary
Cross Validation Report
Model Fit Report

Model Fit Options

Variable Importance Plot
VIP vs Coefficients Plots
Save Columns

Additional Example of Partial Least Squares

Statistical Details for the Partial Least Squares Platform

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)
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