Applies VAST scaling on numeric data — step

step_vast creates a specification of a recipe step that will perform VAST scaling on the columns

step_vast(
  recipe,
  ...,
  scaling = "autoscale",
  role = NA,
  trained = FALSE,
  means = NULL,
  sds = NULL,
  cvs = NULL,
  na_rm = TRUE,
  skip = FALSE,
  id = rand_id("vast")
)

# S3 method for step_vast
tidy(x, ...)

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 which variables are affected by the step. See `selections()` for more details. For the `tidy` method, these are not currently used.
scaling	Either `autoscale` or `pareto`. Controls the scaling method. See notes below.
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.
means	A named numeric vector of means. This is `NULL` until computed by `prep.recipe()`.
sds	A named numeric vector of stadard deviations. This is `NULL` until computed by `prep.recipe()`.
cvs	A named numeric vector of variation coeficients. This is `NULL` until computed by `prep.recipe()`.
na_rm	A logical value indicating whether `NA` values should be removed when computing the standard deviation and mean.
skip	A logical. Should the step be skipped when the recipe is baked by `bake.recipe()`? While all operations are baked when `prep.recipe()` 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.
x	A `step_vast` object.

Value

An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms (the selectors or variables selected), value (the standard deviations and means), and statistic for the type of value.

Details

Variable Stability (VAST) scaling preforms centering and scaling followed by a weighting of each variable by its variation coeficient.

The argument scaling controls which scaling method should be used before variable weighting. autoscale will perform mean-centering and standard deviation scaling while pareto will scale by the square-root of the standard deviation.

References

Keun H. C., Ebbels T. M. D., Antti H., Bollard M. E., Beckonert O., Holmes E., et al. (2003). Improved analysis of multivariate data by variable stability scaling: application to NMR-based metabolic profiling. Anal. Chim. Acta 490, 265–276 10.1016/S0003-2670(03)00094-1 https://www.sciencedirect.com/science/article/abs/pii/S0003267003000941

Examples

library(tidySpectR)
library(recipes)
autoscale_vast <- 
  recipe(Species ~. , iris) %>%
  step_vast(all_predictors(), scaling = 'autoscale')

pareto_vast <- 
  recipe(Species ~. , iris) %>%
  step_vast(all_predictors(), scaling = 'pareto')