Standard Normal Variate (SNV) Transformation

step_measure_snv() creates a specification of a recipe step that applies Standard Normal Variate transformation to spectral data. SNV normalizes each spectrum to have zero mean and unit standard deviation.

Usage

step_measure_snv(
  recipe,
  measures = NULL,
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_snv")
)

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.

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

Standard Normal Variate (SNV) is a row-wise transformation that normalizes each spectrum independently. For a spectrum \(x\), the transformation is:

$$SNV(x) = \frac{x - \bar{x}}{s_x}$$

where \(\bar{x}\) is the mean and \(s_x\) is the standard deviation of the spectrum values.

SNV is commonly used to remove multiplicative effects of scatter and particle size in NIR spectroscopy. After SNV transformation, each spectrum will have a mean of zero and a standard deviation of one.

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().

The measurement locations are preserved unchanged.

Tidying

When you tidy() this step, a tibble with column terms (set to ".measures") and id is returned.

See also

Other measure-preprocessing: step_measure_absorbance(), step_measure_calibrate_x(), step_measure_calibrate_y(), step_measure_derivative(), step_measure_derivative_gap(), step_measure_emsc(), step_measure_kubelka_munk(), step_measure_log(), step_measure_map(), step_measure_msc(), step_measure_normalize_istd(), step_measure_osc(), step_measure_ratio_reference(), step_measure_subtract_blank(), step_measure_subtract_reference(), step_measure_transmittance()

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_snv() |>
  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