The test R2 represents the proportion of variation in the responses that is explained by the original model using predictor values from the test data.
The test data set must include the same number of predictors as the original data set. The test R2 can only be calculated if the test data includes response data for each observation. The test R2 is calculated in the same way as R2.
Test R2 identifies how well the PLS regression model predicts your test data. Higher test R2 values indicate the model has greater predictive ability.
Often, PLS regression is performed in two steps. The first step, sometimes called training, involves calculating a PLS regression model for a sample data set (also called a training data set). The second step involves validating this model with a different set of data, often called a test data set. Some test data sets include response values, others do not. If the test data set does include response values, then Minitab can calculate a test R2.
If you use cross-validation, compare the test R2 to the predicted R2. Ideally, these values should be similar. A test R2 that is significantly smaller than the predicted R2 indicates that cross-validation is overly optimistic about the model's predictive ability or that the two data samples are from different populations.