Partial Least Squares Feature Extraction
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 = deprecated(),
res = NULL,
prefix = "PLS",
keep_original_cols = FALSE,
skip = FALSE,
id = rand_id("pls")
)
## S3 method for class '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 |
Use |
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. |
keep_original_cols |
A logical to keep the original variables in the
output. Defaults to |
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 |
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.
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.
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)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.