recipes (version 0.1.0)

step_kpca: Kernel PCA Signal Extraction

Description

step_kpca a specification of a recipe step that will convert numeric data into one or more principal components using a kernel basis expansion.

Usage

step_kpca(recipe, ..., role = "predictor", trained = FALSE, num = 5,
  res = NULL, options = list(kernel = "rbfdot", kpar = list(sigma = 0.2)),
  prefix = "kPC")

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 which variables will be used to compute the components. See selections for more details.

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

The number of PCA components to retain as new predictors. If num is greater than the number of columns or the number of possible components, a smaller value will be used.

res

An S4 kpca object is stored here once this preprocessing step has be trained by prep.recipe.

options

A list of options to kpca. Defaults are set for the arguments kernel and kpar but others can be passed in. Note that the arguments x and features should not be passed here (or at all).

prefix

A character string that will be the prefix to the resulting new variables. See notes below.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any).

Details

Kernel principal component analysis (kPCA) is an extension 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.

As with ordinary PCA, it is important to standardized the variables prior to running PCA (step_center and step_scale can be used for this purpose).

When performing kPCA, the kernel function (and any important kernel parameters) must be chosen. The kernlab package is used and the reference below discusses the types of kernels available and their parameter(s). These specifications can be made in the kernel and kpar slots of the options argument to step_kpca.

The argument num 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 < 10, their names will be kPC1 - kPC9. If num = 101, the names would be kPC001 - kPC101.

References

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.

See Also

step_pca step_ica step_isomap recipe prep.recipe bake.recipe

Examples

# NOT RUN {
data(biomass)

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_predictors()) %>%
  step_center(all_predictors()) %>%
  step_scale(all_predictors()) %>%
  step_kpca(all_predictors())

kpca_estimates <- prep(kpca_trans, training = biomass_tr)

kpca_te <- bake(kpca_estimates, biomass_te)

rng <- extendrange(c(kpca_te$kPC1, kpca_te$kPC2))
plot(kpca_te$kPC1, kpca_te$kPC2,
     xlim = rng, ylim = rng)
# }