Quantitative Regression Analysis
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Contents |
These methods develop regression models which attempt to predict a quantity based on measurements of responses (x-block) and corresponding quantities (y-block) on known samples.
The y-block may contain a physical quantity which is directly related to the measurements in the x-block, or it may be a value which is indirectly related to the measured x-block values. In the latter case, the resulting model is considered an "inferential" model.
Standard Linear Modeling Methods
- analysis - Graphical user interface for data analysis.
- cls - Classical Least Squares regression for multivariate Y.
- pcr - Principal components regression for multivariate Y.
- pls - Partial least squares regression for multivariate Y.
- mlr - Multiple Linear Regression for multivariate Y.
- crossval - Cross-validation for decomposition and linear regression.
Multiway Models
- npls - Multilinear-PLS (N-PLS) for true multi-way regression.
- ncrossval - Cross-validation for multilinear PLS (N-PLS).
- modelviewer - Visualization tool for multi-way models.
Local, Non-linear, and Other Methods
- cr - Continuum Regression for multivariate y.
- frpcr - Full-ratio PCR calibration and prediction.
- lwrpred - Predictions based on locally weighted regression models.
- polypls - PLS regression with polynomial inner-relation.
- ridge - Ridge regression by Hoerl-Kennard-Baldwin.
Other Topics
- Application of Models to New Data
- Model Analysis and Calculation Utilities
- Plotting Utilities
- Related Tools
(Sub topic of PLS_Toolbox_Topics)