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Standard Normal Variate Recipe Step

Source: R/recipe_steps_preprocessing.R
step_snv.Rd

step_snv() creates a specification of a recipe step that centers and scales each spectrum by its own mean and standard deviation, with snv().

Usage

step_snv(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  columns = NULL,
  skip = FALSE,
  id = recipes::rand_id("snv")
)

Arguments

recipe

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

...

One or more selector functions to choose the predictors (for example, the spectral channels). See recipes::selections().

role

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

trained

A logical indicating whether the step has been trained.

columns

The names of the selected columns, stored once the step has been trained.

skip

A logical. Should the step be skipped when the recipe is baked by recipes::bake()? Keep the default FALSE, so that new data are corrected with the same filter as the training data.

id

A character string that is unique to this step.

Value

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

Details

The selected columns form one spectrum per row; select all the channels of the spectrum and only them. Nothing is estimated from the training data. The selected columns are replaced by the SNV-transformed values. tidy() returns the selected terms and id.

See also

snv()

Examples

data(forageLIBS)
# potassium and the K I resonance lines
wl <- suppressWarnings(as.numeric(names(forageLIBS)))
dat <- forageLIBS[c(which(names(forageLIBS) == "K"), which(wl > 760 & wl < 780))]
rec <- recipes::recipe(K ~ ., data = dat[1:300, ]) |>
  step_snv(recipes::all_predictors())
prepped <- recipes::prep(rec)
recipes::bake(prepped, new_data = dat[301:368, ])
#> # A tibble: 68 × 246
#>    `760.0161416` `760.1001689` `760.1841961` `760.2682233` `760.3522506`
#>            <dbl>         <dbl>         <dbl>         <dbl>         <dbl>
#>  1        -0.376        -0.377        -0.382        -0.383        -0.385
#>  2        -0.362        -0.372        -0.368        -0.368        -0.372
#>  3        -0.385        -0.378        -0.376        -0.384        -0.387
#>  4        -0.382        -0.378        -0.374        -0.382        -0.385
#>  5        -0.384        -0.378        -0.374        -0.369        -0.372
#>  6        -0.378        -0.378        -0.378        -0.381        -0.387
#>  7        -0.381        -0.374        -0.376        -0.382        -0.388
#>  8        -0.373        -0.381        -0.382        -0.381        -0.387
#>  9        -0.375        -0.373        -0.374        -0.370        -0.377
#> 10        -0.384        -0.380        -0.385        -0.381        -0.381
#> # ℹ 58 more rows
#> # ℹ 241 more variables: `760.4362778` <dbl>, `760.520305` <dbl>,
#> #   `760.6043323` <dbl>, `760.6883595` <dbl>, `760.7723867` <dbl>,
#> #   `760.856414` <dbl>, `760.9404412` <dbl>, `761.0244684` <dbl>,
#> #   `761.1084957` <dbl>, `761.1925229` <dbl>, `761.2765501` <dbl>,
#> #   `761.3605774` <dbl>, `761.4446046` <dbl>, `761.5286319` <dbl>,
#> #   `761.6126591` <dbl>, `761.6966863` <dbl>, `761.7807136` <dbl>, …