PLS as a Multivariate Technique
An Example Exploring Prediction
One-Factor NIPALS Model
Two-Factor NIPALS Model
Variable Selection
SIMPLS Fits
Choosing the Number of Factors
Cross Validation
Types of Cross Validation
A Simulation of K-Fold Cross Validation
Validation in the PLS Platform
The NIPALS and SIMPLS Algorithms
Useful Things to Remember About PLS
Chapter 5 Predicting Biological Activity
Background
The Data
Data Table Description
Initial Data Visualization
A First PLS Model
Our Plan
Performing the Analysis
The Partial Least Squares Report
The SIMPLS Fit Report
Other Options
A Pruned PLS Model
Model Fit
Diagnostics
Performance on Data from Second Study
Comparing Predicted Values for the Second Study to Actual Values
Comparing Residuals for Both Studies
Obtaining Additional Insight
Conclusion
Chapter 6 Predicting the Octane Rating of Gasoline
Background
The Data
Data Table Description
Creating a Test Set Indicator Column
Viewing the Data
Octane and the Test Set
Creating a Stacked Data Table
Constructing Plots of the Individual Spectra
Individual Spectra
Combined Spectra
A First PLS Model
Excluding the Test Set
Fitting the Model
The Initial Report
A Second PLS Model
Fitting the Model
High-Level Overview
Diagnostics
Score Scatterplot Matrices
Loading Plots
VIPs
Model Assessment Using Test Set
A Pruned Model
Chapter 7 Equation Chapter 1 Section 1Water Quality in the Savannah River Basin
Background
The Data
Data Table Description
Initial Data Visualization
Missing Response Values
Impute Missing Data
Distributions
Transforming AGPT
Differences by Ecoregion
Conclusions from Visual Analysis and Implications
A First PLS Model for the Savannah River Basin
Our Plan
Performing the Analysis
The Partial Least Squares Report
The NIPALS Fit Report
Defining a Pruned Model
A Pruned PLS Model for the Savannah River Basin
Model Fit
Diagnostics
Saving the Prediction Formulas
Comparing Actual Values to Predicted Values for the Test Set
A First PLS Model for the Blue Ridge Ecoregion
Making the Subset
