(ORG-5412)
Sample OPJ to download to try: Partial Least Squares Regression.opj
Partial least squares (PLS) is a method for constructing predictive models when there are many factors and they are highly collinear. It is useful for variable selection and dimension reduction
There are two primary reason for using PLS
- Prediction
PLS is most commonly used for constructing predictive model when the the information contained in a large number of original variables and they are highly collinear.
- Interpretation
PLS can be used to discover important features of a large data set. It often reveals relationships that were previously unsuspected, thereby allowing interpretations of the data that may not ordinarily result from examination of the data.
To perform partial least squares in Origin, select Statistics: Multivariate Analysis: Partial Least Square
How to
The data in the example are reported in Umetrics (1995); the original source is Lindberg, Persson, and Wold (1983). Suppose that the scientist is researching pollution in the Baltic Sea, and they would like to use the spectra of samples of sea water to determine the amounts of three compounds present in samples from the Baltic Sea: lignin sulfonate (pulp industry pollution), humic acids (natural forest products), and detergent (optical whitener).
The scientist also has data of the spectra emission intensities at different frequencies in sample spectrum (v1-v27)
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- Activate Sheet1 in Book1. Select Statistics: Multivariate Analysis: Partial Least Square to open the Partial Least Square dialog
- In the opened dialog, set column v1 ~ v27 as Independent Variables, column ls, ha, dt as Dependent Variables and set other settings as image below. And click OK button
Interpreting Results
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