Laplacian function kernel PCA signal extraction

step_kpca_laplace() creates a specification of a recipe step that will convert numeric data into one or more principal components using a laplace kernel

Usage

step_kpca_laplace(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  num_comp = 5,
  res = NULL,
  columns = NULL,
  sigma = 0.2,
  prefix = "kPC",
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("kpca_laplace")
)

Arguments

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 selections() for more details.
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 num_comp is greater than the number of columns or the number of possible components, a smaller value will be used. If num_comp = 0 is set then no transformation is done and selected variables will stay unchanged, regardless of the value of keep_original_cols.
res: An S4 kernlab::kpca() object is stored here once this preprocessing step has be trained by prep().
columns: A character string of the selected variable names. This field is a placeholder and will be populated once prep() is used.
sigma: A numeric value for the laplace function parameter.
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 FALSE.
skip: 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 = TRUE as it may affect the computations for subsequent operations.
id: A character string that is unique to this step to identify it.

Usage

Arguments

See also