`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.

`mixOmics¹²`

More information on how parsnip is used for modeling is at https://www.tidymodels.org/.

## Arguments

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

- predictor_prop
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.- num_comp
The number of PLS components to retain.

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

## Details

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)
```