step_xvast: Applies x-VAST scaling on numeric data
In CVUA-RRW/tidySpectR: An R package for tidy chemometrics

Description Usage Arguments Details Value References Examples

step_svast creates a specification of a recipe step that will perform x-VAST scaling on the columns

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

## S3 method for class 'step_xvast'
tidy(x, ...)

`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.
`outcome`	When a single outcome is available, character string or call to `dplyr::vars()` can be used to specify a single outcome variable.
`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_xvast` object.

supervised maximum Variable Stability (x-VAST) scaling preforms centering and scaling followed by a weighting of each variable by the maximum of the class-specific variation coeficients.

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.

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.

Yang, J., Zhao, X., Lu, X., Lin, X., & Xu, G. (2015). A data preprocessing strategy for metabolomics to reduce the mask effect in data analysis. Frontiers in molecular biosciences, 2, 4. https://doi.org/10.3389/fmolb.2015.00004 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4428451/

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

pareto_svast <- 
  recipe(Species ~. , iris) %>%
  step_xvast(all_predictors(), scaling = 'pareto', outcome = 'Species')