Poisson Scaling Recipe Step

step_poisson_scale() creates a specification of a recipe step that divides each selected column by the square root of its mean plus an offset, as poisson_scale() does (column mode).

Usage

step_poisson_scale(
  recipe,
  ...,
  role = NA,
  trained = FALSE,
  offset = 3,
  scales = NULL,
  columns = NULL,
  skip = FALSE,
  id = recipes::rand_id("poisson_scale")
)

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.

offset

The offset, in percent of the largest column mean. Default is 3.

scales

The scale of each column, stored once 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

Poisson scaling suits count data, whose variance grows with the mean. The scales \(\sqrt{\bar{x}_j + c}\) are estimated from the training data, where the offset \(c\) is offset percent of the largest column mean, and are applied unchanged to new data. tidy() returns the selected terms, the scale each column is divided by (NA before the step is trained) and id.

See also

poisson_scale(), step_pareto_scale()

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_poisson_scale(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          21.1          20.9          20.4          20.3          20.0
#>  2          20.4          19.3          19.7          19.7          19.3
#>  3          21.9          22.7          23.1          22.0          21.6
#>  4          20.6          21.1          21.6          20.6          20.3
#>  5          19.9          20.6          21.1          21.7          21.4
#>  6          23.4          23.3          23.3          22.9          22.1
#>  7          21.5          22.3          22.0          21.3          20.7
#>  8          22.1          21.2          21.0          21.2          20.5
#>  9          19.9          20.1          20.0          20.4          19.8
#> 10          21.6          22.1          21.6          22.0          22.1
#> # 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>, …