Matlab routines



This is a list of the main routines you can use to build models by means of the PCA toolbox for MATLAB:

pca_gui

pca_gui opens a GUI for calculating all models provided in the toolbox; in order to open the graphical interface, type:

pca_gui

on the MATLAB command window. There are no inputs; data, labels and models can be loaded and saved directly from the graphical interface.

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Routines for fitting models

Principal Component Analysis (PCA), Muiltidimensional Scaling (MDS) and Cluster Analysis can be calculated by means of the routines pca_model, mds_model and cluser_model respectively. The output of the routines collects the calculated results in a matlab structure. The pca_compsel routine performs and calculate different parameters to estimate the optimal number of components to be retained. Type "help routine_name" on the MATLAB command window for further information.

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Routines for predicting new samples (for PCA)

Prediction of new samples by means of PCA can be performed with the routine pca_project. The output of the routine collects the scores of the predicted samples in the PCA model and other details (such as Q residuals and T2 Hotelling values). Type "help pca_project " on the MATLAB command window for further informations.

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