step_kpca_poly {recipes} | R Documentation |
step_kpca_poly
creates a specification of a recipe step that
will convert numeric data into one or more principal components
using a polynomial kernel basis expansion.
step_kpca_poly( recipe, ..., role = "predictor", trained = FALSE, num_comp = 5, res = NULL, columns = NULL, degree = 2, scale_factor = 1, offset = 1, prefix = "kPC", keep_original_cols = FALSE, skip = FALSE, id = rand_id("kpca_poly") )
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 |
res |
An S4 |
columns |
A character string of variable names that will be populated elsewhere. |
degree, scale_factor, offset |
Numeric values for the polynomial kernel function. |
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. |
Kernel principal component analysis (kPCA) is an extension of a PCA analysis that conducts the calculations in a broader dimensionality defined by a kernel function. For example, if a quadratic kernel function were used, each variable would be represented by its original values as well as its square. This nonlinear mapping is used during the PCA analysis and can potentially help find better representations of the original data.
This step requires the kernlab package. If not installed, the step will stop with a prompt about installing the package.
As with ordinary PCA, it is important to center and scale the variables
prior to computing PCA components (step_normalize()
can be used for
this purpose).
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 when keep_original_cols = FALSE
. 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
, the
new names will be kPC1
- kPC9
. If num_comp = 101
, the names would be
kPC001
- kPC101
.
An updated version of recipe
with the new step added to the
sequence of any existing operations.
When you tidy()
this step, a tibble with column
terms
(the selectors or variables selected) is returned.
The underlying operation does not allow for case weights.
Scholkopf, B., Smola, A., and Muller, K. (1997). Kernel principal component analysis. Lecture Notes in Computer Science, 1327, 583-588.
Karatzoglou, K., Smola, A., Hornik, K., and Zeileis, A. (2004). kernlab - An S4 package for kernel methods in R. Journal of Statistical Software, 11(1), 1-20.
Other multivariate transformation steps:
step_classdist()
,
step_depth()
,
step_geodist()
,
step_ica()
,
step_isomap()
,
step_kpca_rbf()
,
step_kpca()
,
step_mutate_at()
,
step_nnmf_sparse()
,
step_nnmf()
,
step_pca()
,
step_pls()
,
step_ratio()
,
step_spatialsign()
library(ggplot2) data(biomass, package = "modeldata") biomass_tr <- biomass[biomass$dataset == "Training", ] biomass_te <- biomass[biomass$dataset == "Testing", ] rec <- recipe( HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur, data = biomass_tr ) kpca_trans <- rec %>% step_YeoJohnson(all_numeric_predictors()) %>% step_normalize(all_numeric_predictors()) %>% step_kpca_poly(all_numeric_predictors()) kpca_estimates <- prep(kpca_trans, training = biomass_tr) kpca_te <- bake(kpca_estimates, biomass_te) ggplot(kpca_te, aes(x = kPC1, y = kPC2)) + geom_point() + coord_equal() tidy(kpca_trans, number = 3) tidy(kpca_estimates, number = 3)