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pls() defines a partial least squares model that uses latent variables to model the data. It is similar to a supervised version of principal component. This function can fit classification and regression models.

There are different ways to fit this model, and the method of estimation is chosen by setting the model engine. The engine-specific pages for this model are listed below.

¹ The default engine. ² Requires a parsnip extension package for classification and regression.

More information on how parsnip is used for modeling is at


  mode = "unknown",
  predictor_prop = NULL,
  num_comp = NULL,
  engine = "mixOmics"



A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification".


The maximum proportion of original predictors that can have non-zero coefficients for each PLS component (via regularization). This value is used for all PLS components for X.


The number of PLS components to retain.


A single character string specifying what computational engine to use for fitting.


This function only defines what type of model is being fit. Once an engine is specified, the method to fit the model is also defined. See set_engine() for more on setting the engine, including how to set engine arguments.

The model is not trained or fit until the fit() function is used with the data.

Each of the arguments in this function other than mode and engine are captured as quosures. To pass values programmatically, use the injection operator like so:

value <- 1
pls(argument = !!value)