step_ica {recipes} | R Documentation

## 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, num = NULL,
      prefix = "IC", skip = FALSE, id = rand_id("ica"))

    ## S3 method for class '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()`.
`num` | The number of components to retain (this will be deprecated in factor of `num_comp` in version 0.1.5). `num_comp` will override this option.
`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.

### 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`.

### 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`.

### 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

    # 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)
    }

[Package recipes version 0.1.5 Index]