PLS

Partial least squares regression

This procedure estimates partial least squares (PLS, also known as "projection to latent structure") regression models. PLS is a predictive technique that is an alternative to ordinary least squares (OLS) regression, canonical correlation, or structural equation modeling, and it is particularly useful when predictor variables are highly correlated or when the number of predictors exceeds the number of cases.

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Requirements

• IBM SPSS Statistics 18 or later, the corresponding IBM SPSS Statistics-Integration Plug-in for Python, and the NumPy and SciPy Python packages. Instructions for obtaining NumPy and SciPy and special configuration instructions for users of IBM SPSS Statistics version 22 or higher are provided in the document enabling_pls.pdf, located in the PLS directory under the location where the PLS extension command is installed (see the output from the SHOW EXTPATHS command for a listing of possible locations).

Note: For users with IBM SPSS Statistics version 21 or higher, the PLS extension bundle is installed as part of IBM SPSS Statistics-Essentials for Python.

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Installation intructions

1. Open IBM SPSS Statistics
2. Navigate to Utilities -> Extension Bundles -> Download and Install Extension Bundles
3. Search for the name of the extension and click Ok. Your extension will be available.

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License

• Apache 2.0

Contributors

• IBM SPSS JKP, JMB