Mean Centering

step_measure_center() creates a specification of a recipe step that subtracts the mean at each measurement location (column-wise centering). The means are computed from the training data and applied to new data.

Usage

step_measure_center(
  recipe,
  measures = NULL,
  role = NA,
  trained = FALSE,
  learned_params = NULL,
  skip = FALSE,
  id = recipes::rand_id("measure_center")
)

Arguments

recipe

A recipe object. The step will be added to the sequence of operations for this recipe.

measures

An optional character vector of measure column names to process. If NULL (the default), all measure columns (columns with class measure_list) will be processed. Use this to limit processing to specific measure columns when working with multiple measurement types.

role

Not used by this step since no new variables are created.

trained

A logical to indicate if the quantities for preprocessing have been estimated.

learned_params

A named list containing learned means and locations for each measure column. This is NULL until the step is trained.

skip

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

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Details

Mean centering is a fundamental preprocessing step for multivariate analysis methods like PCA and PLS. It removes the average signal at each measurement location.

For a data matrix \(X\) with samples as rows and measurement locations as columns, the transformation is:

$$X_{centered} = X - \bar{X}$$

where \(\bar{X}\) is the column-wise mean computed from the training data.

The means are learned during prep() from the training data and stored for use when applying the transformation to new data during bake().

No selectors should be supplied to this step function. The data should be in the internal format produced by step_measure_input_wide() or step_measure_input_long().

Tidying

When you tidy() this step after training, a tibble with the learned means at each location is returned.

See also

step_measure_scale_auto(), step_measure_scale_pareto()

Other measure-scaling: step_measure_scale_auto(), step_measure_scale_pareto(), step_measure_scale_range(), step_measure_scale_vast()

Examples

library(recipes)

rec <-
  recipe(water + fat + protein ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_center() |>
  prep()

bake(rec, new_data = NULL)
#> # A tibble: 215 × 5
#>       id water   fat protein .measures
#>    <int> <dbl> <dbl>   <dbl>    <meas>
#>  1     1  60.5  22.5    16.7 [100 × 2]
#>  2     2  46    40.1    13.5 [100 × 2]
#>  3     3  71     8.4    20.5 [100 × 2]
#>  4     4  72.8   5.9    20.7 [100 × 2]
#>  5     5  58.3  25.5    15.5 [100 × 2]
#>  6     6  44    42.7    13.7 [100 × 2]
#>  7     7  44    42.7    13.7 [100 × 2]
#>  8     8  69.3  10.6    19.3 [100 × 2]
#>  9     9  61.4  19.9    17.7 [100 × 2]
#> 10    10  61.4  19.9    17.7 [100 × 2]
#> # ℹ 205 more rows