recipes (version 0.1.7)

step_ica: ICA Signal Extraction

Description

step_ica creates a specification of a recipe step that will convert numeric data into one or more independent components.

Usage

step_ica(recipe, ..., role = "predictor", trained = FALSE,
  num_comp = 5, options = list(), res = NULL, prefix = "IC",
  skip = FALSE, id = rand_id("ica"))
# S3 method for step_ica
tidy(x, ...)

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. For the tidy method, these are not currently used.

role

For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new independent 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 ICA 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.

options

A list of options to fastICA::fastICA(). No defaults are set here. Note that the arguments X and n.comp should not be passed here.

res

The fastICA::fastICA() object is stored here once this preprocessing step has be trained by prep.recipe().

prefix

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

skip

A logical. Should the step be skipped when the recipe is baked by bake.recipe()? While all operations are baked when prep.recipe() 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.

x

A step_ica object.

Value

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.

Details

Independent component analysis (ICA) is a transformation of a group of variables that produces a new set of artificial features or components. ICA assumes that the variables are mixtures of a set of distinct, non-Gaussian signals and attempts to transform the data to isolate these signals. Like PCA, the components are statistically independent from one another. This means that they can be used to combat large inter-variables correlations in a data set. Also like PCA, it is advisable to center and scale the variables prior to running ICA.

This package produces components using the "FastICA" methodology (see reference below). This step requires the dimRed and fastICA packages. If not installed, the step will stop with a note about installing these packages.

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 IC1 - IC9. If num_comp = 101, the names would be IC001 - IC101.

References

Hyvarinen, A., and Oja, E. (2000). Independent component analysis: algorithms and applications. Neural Networks, 13(4-5), 411-430.

See Also

step_pca() step_kpca() step_isomap() recipe() prep.recipe() bake.recipe()

Examples

# NOT RUN {
# from fastICA::fastICA
set.seed(131)
S <- matrix(runif(400), 200, 2)
A <- matrix(c(1, 1, -1, 3), 2, 2, byrow = TRUE)
X <- as.data.frame(S %*% A)

tr <- X[1:100, ]
te <- X[101:200, ]

rec <- recipe( ~ ., data = tr)

ica_trans <- step_center(rec,  V1, V2)
ica_trans <- step_scale(ica_trans, V1, V2)
ica_trans <- step_ica(ica_trans, V1, V2, num_comp = 2)

if (require(dimRed) & require(fastICA)) {
  ica_estimates <- prep(ica_trans, training = tr)
  ica_data <- bake(ica_estimates, te)

  plot(te$V1, te$V2)
  plot(ica_data$IC1, ica_data$IC2)

  tidy(ica_trans, number = 3)
  tidy(ica_estimates, number = 3)
}
# }