step_pls | R Documentation |
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,
columns = NULL,
prefix = "PLS",
keep_original_cols = FALSE,
skip = FALSE,
id = rand_id("pls")
)
recipe |
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 |
role |
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. |
trained |
A logical to indicate if the quantities for preprocessing have been estimated. |
num_comp |
The number of components 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 |
columns |
A character string of the selected variable names. This field
is a placeholder and will be populated once |
prefix |
A character string for the prefix of 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. |
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 PLS1
- 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.
An updated version of recipe
with the new step added to the
sequence of any existing operations.
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. When you tidy()
this step, a tibble with columns terms
(the
selectors or variables selected), components
, and values
is returned.
This step has 2 tuning parameters:
num_comp
: # Components (type: integer, default: 2)
predictor_prop
: Proportion of Predictors (type: double, default: 1)
The underlying operation does not allow for case weights.
https://en.wikipedia.org/wiki/Partial_least_squares_regression
Rohart F, Gautier B, Singh A, LĂȘ Cao K-A (2017) mixOmics: An R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1371/journal.pcbi.1005752")}
Other multivariate transformation steps:
step_classdist_shrunken()
,
step_classdist()
,
step_depth()
,
step_geodist()
,
step_ica()
,
step_isomap()
,
step_kpca_poly()
,
step_kpca_rbf()
,
step_kpca()
,
step_mutate_at()
,
step_nnmf_sparse()
,
step_nnmf()
,
step_pca()
,
step_ratio()
,
step_spatialsign()
# 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)
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