Partial Least Squares Feature ExtractionSource:
step_pls creates a specification of a recipe step that will
convert numeric data into one or more new dimensions.
A recipe object. The step will be added to the sequence of operations for this recipe.
One or more selector functions to choose variables for this step. See
selections()for more details.
For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
A logical to indicate if the quantities for preprocessing have been estimated.
The number of components to retain as new predictors. If
num_compis greater than the number of columns or the number of possible components, a smaller value will be used. If
num_comp = 0is set then no transformation is done and selected variables will stay unchanged.
The maximum number of original predictors that can have non-zero coefficients for each PLS component (via regularization).
When a single outcome is available, character string or call to
dplyr::vars()can be used to specify a single outcome variable.
keep_original_colsinstead to specify whether the original predictor data should be retained along with the new features.
A list of results are stored here once this preprocessing step has been trained by
A character string of variable names that will be populated elsewhere.
A character string for the prefix of the resulting new variables. See notes below.
A logical to keep the original variables in the output. Defaults to
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked when
prep()is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using
skip = TRUEas it may affect the computations for subsequent operations.
A character string that is unique to this step to identify it.
An updated version of
recipe with the new step added to the
sequence of any existing operations.
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.
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
num_comp = 101, the
names would be
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::splsda(). See the references in
mixOmics::spls() for details.
tidy() method returns the coefficients that are
usually defined as
(See the Wikipedia article below)
When applied to data, these values are usually scaled by a column-specific
tidy() method applies this same norm to the coefficients shown
above. When you
tidy() this step, a tibble with columns
selectors or variables selected),
values is returned.
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. doi: 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_numeric_predictors(), outcome = "HHV", num_comp = 3) sparse_pls <- recipe(HHV ~ ., data = biom_tr) %>% step_pls(all_numeric_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_numeric_predictors(), outcome = "class", num_comp = 5) sparse_plsda <- recipe(class ~ ., data = cell_tr) %>% step_pls(all_numeric_predictors(), outcome = "class", num_comp = 5, predictor_prop = 1/4)