Description Usage Arguments Details Value See Also Examples
step_pca_sparse()
creates a specification of a recipe step that will convert
numeric data into one or more principal components that can have some zero
coefficients.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  step_pca_sparse(
recipe,
...,
role = "predictor",
trained = FALSE,
num_comp = 5,
predictor_prop = 1,
options = list(),
res = NULL,
prefix = "PC",
keep_original_cols = FALSE,
skip = FALSE,
id = rand_id("pca_sparse")
)
## S3 method for class 'step_pca_sparse'
tidy(x, ...)
## S3 method for class 'step_pca_sparse'
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 components. See 
role 
For model terms created by this step, what analysis role should they be assigned? By default, the function assumes that the new principal component 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 PCA components to retain as new predictors.
If 
predictor_prop 
The maximum number of original predictors that can have nonzero coefficients for each PCA component (via regularization). 
options 
A list of options to the default method for 
res 
The rotation matrix once this
preprocessing step has be 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 
The irlba
package is required for this step. If it is not installed, the user
will be prompted to do so when the step is defined. The irlba::ssvd()
function is
used to encourage sparsity; that documentation has details about this method.
The argument num_comp
controls the number of components that
will be retained (per default 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 PC1
 PC9
.
If num_comp = 101
, the names would be PC001

PC101
.
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), value
(the
loading), and component
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  library(recipes)
library(ggplot2)
data(ad_data, package = "modeldata")
ad_rec <
recipe(Class ~ ., data = ad_data) %>%
step_zv(all_predictors()) %>%
step_YeoJohnson(all_numeric_predictors()) %>%
step_normalize(all_numeric_predictors()) %>%
step_pca_sparse(all_numeric_predictors(),
predictor_prop = 0.75,
num_comp = 3,
id = "sparse pca") %>%
prep()
tidy(ad_rec, id = "sparse pca") %>%
mutate(value = ifelse(value == 0, NA, value)) %>%
ggplot(aes(x = component, y = terms, fill = value)) +
geom_tile() +
scale_fill_gradient2() +
theme(axis.text.y = element_blank())

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