Gaussian Kernel Smoothing

step_measure_smooth_gaussian() creates a specification of a recipe step that applies Gaussian kernel smoothing. This produces smooth results while preserving the general shape of peaks.

Usage

step_measure_smooth_gaussian(
  recipe,
  measures = NULL,
  sigma = 1,
  window = NULL,
  edge_method = c("reflect", "constant", "NA"),
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_smooth_gaussian")
)

Arguments

recipe

A recipe object.

measures

An optional character vector of measure column names.

sigma

The standard deviation of the Gaussian kernel. Default is 1. Larger values produce more smoothing. Tunable via smooth_sigma().

window

The window size. If NULL (default), automatically set to ceiling(6 * sigma) | 1 (6 sigma rule, ensuring odd).

edge_method

How to handle edges. One of "reflect" (default), "constant", or "NA".

role

Not used.

trained

Logical indicating if the step has been trained.

skip

Logical. Should the step be skipped when baking?

id

Unique step identifier.

Value

An updated recipe with the new step added.

Details

Gaussian smoothing convolves the spectrum with a Gaussian kernel: $$G(x) = \exp(-x^2 / 2\sigma^2)$$

The kernel is normalized to sum to 1. This provides smooth, natural-looking results that preserve peak shapes better than moving average.

See also

Other measure-smoothing: step_measure_despike(), step_measure_filter_fourier(), step_measure_savitzky_golay(), step_measure_smooth_ma(), step_measure_smooth_median(), step_measure_smooth_wavelet()

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_smooth_gaussian(sigma = 2) |>
  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