step_ica
creates a specification of a recipe step
that will convert numeric data into one or more independent
components.
step_ica( recipe, ..., role = "predictor", trained = FALSE, num_comp = 5, options = list(method = "C"), res = NULL, prefix = "IC", skip = FALSE, id = rand_id("ica") ) # S3 method for step_ica tidy(x, ...)
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

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 
options  A list of options to

res  The 
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 
id  A character string that is unique to this step to identify it. 
x  A 
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
.
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, nonGaussian 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 intervariables 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
.
Hyvarinen, A., and Oja, E. (2000). Independent component analysis: algorithms and applications. Neural Networks, 13(45), 411430.
# 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) }#>#>#>#>#>#> #>#>#> #>#>#> #>#>#> # A tibble: 4 x 4 #> terms component value id #> <chr> <chr> <dbl> <chr> #> 1 V1 IC1 1.04 ica_3zAWC #> 2 V1 IC2 0.378 ica_3zAWC #> 3 V2 IC1 0.773 ica_3zAWC #> 4 V2 IC2 0.788 ica_3zAWC