# Method table for Partial Least Squares Regression

Find definitions and interpretation guidance for the Method table.

## Cross-validation

Cross-validation calculates the predictive ability of potential models to help you determine the appropriate number of components to retain in your model. Use cross-validation if you do not know the optimal number of components for your data. If the data contain multiple response variables, Minitab validates the components for all responses simultaneously. For more information, go to Cross-validation in PLS regression.

Minitab can perform three different cross-validation methods:
• Leave-one-out: Calculates potential models leaving out one observation at a time.
• Leave observations out of size: You determine the number of observations that will be excluded each time the model is recalculated. Because this method reduces the number of times it has to recalculate a model, it is most appropriate when you have a large data set.
• Leave out as specified in column: Calculates the models by simultaneously excluding observations that have matching numbers in the group identifier column. This method allows you to specify which observations are omitted together. For example, if the group identifier column includes numbers 1, 2, and 3, all observations with 1 are omitted together and the model is recalculated. Next, all observations with 2 are omitted and the model is recalculated, and so on.

## Components

The components to evaluate or calculate shows whether Minitab determines the number of components in the model or whether you specified the number of components to be included in the model.

### Interpretation

This list shows the possible values and their meanings.
• Set: Minitab evaluates or calculates 10 components.
• User specified: Minitab evaluates or calculates the number of components that you specified.
• Adjusted: Minitab evaluates or calculates the number of components that equals the number of terms. Minitab shows this value when the number that you specify is greater than the number of terms. The number of components cannot be greater than the number of terms.

## Number of components

The components Minitab displays depends on whether you perform cross-validation. For more information, go to Cross-validation in PLS regression.

If you do not perform cross-validation, Minitab displays the number of components that are calculated. This is the number you specify. If you don't specify a number, it is 10 (default) or the number of terms, whichever is less.

If you perform cross-validation, Minitab displays the following:
• The number of components to evaluate. This equals the maximum number of components you specify or, by default, 10 components or the number of terms, whichever is less.
• The number of components selected by cross-validation, based on the model with the highest predicted R2.
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