[Xloadings, Yloadings, Xscores, Yscores, beta, PCTVAR, MSE, stats] = ... plsregress(X, Y, optimalComponents); % Calculate predicted values Y_hat = [ones(numSamples,1) X] * beta; % Calculate R-squared value ss_residual = sum((Y - Y_hat).^2); ss_total = sum((Y - mean(Y)).^2); r_squared = 1 - (ss_residual / ss_total); fprintf('Model trained with %d components. R² = %.3f\n', optimalComponents, r_squared); Use code with caution. Step 4: Diagnostic Plotting
The toolbox uses to store data along with metadata like class labels, axes, and titles, making it easier to manage complex datasets. Key Resources PLS_Toolbox - Third-Party Products & Services - MathWorks matlab pls toolbox
Select an appropriate CV strategy (e.g., Venetian blinds, Leave-one-out, or Random subsets) to determine the optimal number of Latent Variables (LVs) or Principal Components. This step prevents overfitting. Step 4: Diagnostic Plotting The toolbox uses to