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Impute Missing Values in Measurements

Source: R/qc.R
step_measure_impute.Rd

step_measure_impute() creates a specification of a recipe step that imputes (fills in) missing values (NA) in measurement data using interpolation or other methods.

Usage

step_measure_impute(
  recipe,
  measures = NULL,
  method = c("linear", "spline", "constant", "mean"),
  max_gap = Inf,
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_impute")
)

Arguments

recipe

A recipe object.

measures

An optional character vector of measure column names.

method

Imputation method:

  • "linear" (default): Linear interpolation

  • "spline": Cubic spline interpolation

  • "constant": Nearest non-NA value

  • "mean": Global mean of non-NA values

max_gap

Maximum gap size to impute. Gaps larger than this are left as NA. Default is Inf (impute all).

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

Missing values can occur due to:

  • Removed spikes (after despiking with replacement set to NA)

  • Excluded regions

  • Instrument gaps or dropouts

Linear and spline interpolation use the stats::approx() and stats::spline() functions respectively. They are most appropriate when gaps are small relative to spectral features.

See also

Other measure-qc: step_measure_qc_outlier(), step_measure_qc_saturated(), step_measure_qc_snr()

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_impute(method = "linear") |>
  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