Select the analysis options for Partial Least Squares Regression

Stat > Regression > Partial Least Squares > Options

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 to determine 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:

None: Do not perform cross-validation.
Leave-one-out: Use this option to calculate potential models leaving out one observation at a time. For large data sets, this method can be time-consuming, because it recalculates the models as many times as there are observations.
Leave-group-out of size: Enter 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: Use this option to calculate 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.

Type of coding for categorical predictors

To perform the analysis, Minitab needs to recode the categorical predictors using one of two methods. Consider changing the method based on whether you want to compare the levels of the predictor to the overall mean or the mean of a reference level. For more information, go to Coding schemes for categorical predictors.

(1, 0): Choose to estimate the difference between each level mean and the reference level's mean. If you choose the (1, 0) coding scheme, you can specify the reference level.
(-1, 0, +1): Choose to estimate the difference between each level mean and the overall mean.

Reference level (enter categorical predictor followed by level)

Enter the reference level by typing the categorical predictor column followed by the reference level. (Text and date/time levels must be enclosed in quotes.) You can assign a reference level only when you use 1, 0 coding. By default, Minitab sets the following reference levels based on the data type:

For numeric categorical predictors, the reference level is the level with the least numeric value.
For date/time categorical predictors, the reference level is the level with the earliest date/time.
For text categorical predictors, the reference level is the level that is first in alphabetical order.