Data considerations for Partial Least Squares Regression

To ensure that your results are valid, consider the following guidelines when you collect data, perform the analysis, and interpret your results.

The predictors do not need to be fixed

PLS does not assume that the predictors are fixed, which means that the predictors can be measured with error. If the predictors are fixed and do not have considerable measurement error, use Fit Regression Model.

The data may have more terms than observations or the predictors may be highly collinear

If the predictors are not highly collinear and your data include more observations than the number of predictors, use Fit Regression Model.

The predictors can be continuous or categorical

A continuous variable can be measured and ordered, and has an infinite number of values between any two values. For example, the diameters of a sample of tires is a continuous variable.

Categorical variables contain a finite, countable number of categories or distinct groups. Categorical data might not have a logical order. For example, categorical predictors include gender, material type, and payment method.

If you have a discrete variable, you can decide whether to treat it as a continuous or categorical predictor. A discrete variable can be measured and ordered but it has a countable number of values. For example, the number of people that live in a household is a discrete variable. The decision to treat a discrete variable as continuous or categorical depends on the number of levels, as well as the purpose of the analysis. For more information, go to What are categorical, discrete, and continuous variables?.

The response variables should be continuous

If you perform the analysis with correlated response variables, PLS can detect multivariate response patterns and weaker relationships than are possible with a separate analysis for each response.

If the response variable is categorical, your model is less likely to meet the assumptions of the analysis, to accurately describe your data, or to make useful predictions.

If your predictors are not highly correlated and you do not have more predictors than observations, you can consider the following alternative analyses.

• If your response variable has two categories, such as pass and fail, use Fit Binary Logistic Model.
• If your response variable contains three or more categories that have a natural order, such as strongly disagree, disagree, neutral, agree, and strongly agree, use Ordinal Logistic Regression.
• If your response variable contains three or more categories that do not have a natural order, such as scratch, dent, and tear, use Nominal Logistic Regression.
• If your response variable counts occurrences, such as the number of defects, use Fit Poisson Model.

Collect data using best practices

To ensure that your results are valid, consider the following guidelines:

• Make certain that the data represent the population of interest.
• Collect enough data to provide the necessary precision.
• Measure variables as accurately and precisely as possible.
• Record the data in the order it is collected.

The model should provide a good fit to the data

If the model does not fit the data, the results can be misleading. In the output, use residual plots, model selection and validation statistics, and the response plot to determine how well the model fits the data.