`step_pls`

creates a *specification* of a recipe step that will
convert numeric data into one or more new dimensions.

step_pls( recipe, ..., role = "predictor", trained = FALSE, num_comp = 2, predictor_prop = 1, outcome = NULL, options = list(scale = TRUE), preserve = FALSE, res = NULL, prefix = "PLS", skip = FALSE, id = rand_id("pls") ) # S3 method for step_pls tidy(x, ...)

recipe | A recipe object. The step will be added to the sequence of operations for this recipe. |
---|---|

... | One or more selector functions to choose which variables will be
used to compute the dimensions. See |

role | For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new dimension columns created by the original variables will be used as predictors in a model. |

trained | A logical to indicate if the quantities for preprocessing have been estimated. |

num_comp | The number of pls dimensions to retain as new predictors.
If |

predictor_prop | The maximum number of original predictors that can have non-zero coefficients for each PLS component (via regularization). |

outcome | When a single outcome is available, character
string or call to |

options | A list of options to |

preserve | A single logical: should the original predictor data be retained along with the new features? |

res | A list of results are stored here once this preprocessing step
has been trained by |

prefix | A character string that will be the prefix to the resulting new variables. See notes below. |

skip | A logical. Should the step be skipped when the
recipe is baked by |

id | A character string that is unique to this step to identify it. |

x | A |

An updated version of `recipe`

with the new step
added to the sequence of existing steps (if any). For the
`tidy`

method, a tibble with columns `terms`

(the
selectors or variables selected), `components`

, and `values`

.

PLS is a supervised version of principal component analysis that requires the outcome data to compute the new features.

This step requires the Bioconductor mixOmics package. If not installed, the step will stop with a note about installing the package.

The argument `num_comp`

controls the number of components that will
be retained (the original variables that are used to derive the
components are removed from the data). The new components will
have names that begin with `prefix`

and a sequence of numbers.
The variable names are padded with zeros. For example, if `num_comp < 10`

, their names will be `PLS1`

- `PLS9`

. If `num_comp = 101`

, the
names would be `PLS001`

- `PLS101`

.

Sparsity can be encouraged using the `predictor_prop`

parameter. This affects
each PLS component, and indicates the maximum proportion of predictors with
non-zero coefficients in each component. `step_pls()`

converts this
proportion to determine the `keepX`

parameter in `mixOmics::spls()`

and
`mixOmics::splsda()`

. See the references in `mixOmics::spls()`

for details.

The `tidy()`

method returns the coefficients that are usually defined as

$$W(P'W)^{-1}$$

(See the Wikipedia article below)

When applied to data, these values are usually scaled by a column-specific
norm. The `tidy()`

method applies this same norm to the coefficients shown
above.

https://en.wikipedia.org/wiki/Partial_least_squares_regression

Rohart F, Gautier B, Singh A, Lê Cao K-A (2017) *mixOmics: An R package for
'omics feature selection and multiple data integration*. PLoS Comput Biol
13(11): e1005752. https://doi.org/10.1371/journal.pcbi.1005752

# requires the Bioconductor mixOmics package data(biomass, package = "modeldata") biom_tr <- biomass %>% dplyr::filter(dataset == "Training") %>% dplyr::select(-dataset,-sample) biom_te <- biomass %>% dplyr::filter(dataset == "Testing") %>% dplyr::select(-dataset,-sample,-HHV) dense_pls <- recipe(HHV ~ ., data = biom_tr) %>% step_pls(all_predictors(), outcome = "HHV", num_comp = 3) sparse_pls <- recipe(HHV ~ ., data = biom_tr) %>% step_pls(all_predictors(), outcome = "HHV", num_comp = 3, predictor_prop = 4/5) ## ----------------------------------------------------------------------------- ## PLS discriminant analysis data(cells, package = "modeldata") cell_tr <- cells %>% dplyr::filter(case == "Train") %>% dplyr::select(-case) cell_te <- cells %>% dplyr::filter(case == "Test") %>% dplyr::select(-case,-class) dense_plsda <- recipe(class ~ ., data = cell_tr) %>% step_pls(all_predictors(), outcome = "class", num_comp = 5) sparse_plsda <- recipe(class ~ ., data = cell_tr) %>% step_pls(all_predictors(), outcome = "class", num_comp = 5, predictor_prop = 1/4)