Choosing the correct number of Latent Variables (LVs) or Principal Components is the most critical step in PLS modeling.
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Extends standard PLS to multi-dimensional arrays, such as batch process data or excitation-emission fluorescence spectra.
A common question from MATLAB users is how the PLS Toolbox compares to the built-in plsregress function found in the Statistics and Machine Learning Toolbox.
In the world of high-dimensional data analysis, few challenges are as persistent as the "curse of dimensionality." When you have hundreds or thousands of predictor variables (e.g., spectral wavelengths, sensor outputs) but only a handful of samples, standard regression techniques like Ordinary Least Squares (OLS) fail. Enter regression—a multivariate workhorse that has become the gold standard in chemometrics, bioinformatics, and process engineering. matlab pls toolbox
Includes automated routines for Venetian blinds, leave-one-out, and random subset cross-validation to prevent model overfitting.
Predicts continuous responses by projecting predictor variables to a new latent space that maximizes covariance with the response.
One-click plot generation for scores, loadings, residuals, and variable importance in projection (VIP). Command-Line Implementation Example
% Preprocessing: Apply SNV to X and mean-centering to Y X_obj = preprocess(X_obj, 'snv'); Y_obj = preprocess(Y_obj, 'mean center'); Choosing the correct number of Latent Variables (LVs)
: Raw data—especially from hyperspectral imaging or near-IR spectroscopy—is often noisy. The toolbox offers robust methods for baseline correction, smoothing, and normalization.
You can chain methods: detrend, normalize, standard normal variate (SNV), and then a Savitzky–Golay derivative—all without writing complex loops.
% Plot Q residuals vs. Hotelling's T2 plot(model, 'contribution', 'qresiduals');
It bridges the gap between raw data collection (such as spectroscopy, chromatography, or industrial process sensors) and statistical interpretation. The toolbox features both a command-line interface for programmatic automation and an intuitive Graphical User Interface (GUI) called the Analysis Window for point-and-click exploration. Core Algorithms and Functionalities In the world of high-dimensional data analysis, few
Developed by Eigenvector Research, the PLS Toolbox is the gold-standard add-on for MATLAB when it comes to multivariate analysis. While MATLAB’s native Statistics and Machine Learning Toolbox includes plsregress , the PLS Toolbox transforms MATLAB into a dedicated, powerhouse environment for advanced data exploration.
: Beyond standard Partial Least Squares (PLS), it includes tools for: PLS-DA (Discriminant Analysis) for classification tasks.
Evaluate model quality by inspecting the scores plot (to detect outliers or groupings) and the predicted vs. measured plot.