3. PLS Results

So you ran a PLS analysis and got some results. Congratulations! The easy part is done. 🙃 Interpreting (trying to interpret) the results of a PLS analysis—similar to interpreting the results of a PCA or factor analysis or CCA or any other complex decomposition—can be difficult. The pyls package contains some functions, tools, and data structures to try and help.

The PLSResults data structure is, at its core, a Python dictionary that is designed to contain all possible results from any of the analyses available in pyls.types. Let’s generate a small example results object to play around with. We’ll use the dataset from the Behavioral PLS example:

>>> from pyls.examples import load_dataset
>>> data = load_dataset('linnerud')

We can generate the results file by running the behavioral PLS analysis again. We pass the verbose=False flag to suppress the progress bar that would normally be displayed:

>>> from pyls import behavioral_pls
>>> results = behavioral_pls(**data, verbose=False)
>>> results
PLSResults(x_weights, y_weights, x_scores, y_scores, y_loadings, singvals, varexp, permres, bootres, cvres, inputs)

Printing the results object gives us a helpful view of some of the different outputs available to us. While we won’t go into detail about all of these (see the Reference API for info on those), we’ll touch on a few of the potentially more confusing ones.