Description Usage Arguments Details Value References Examples
step_pareto
creates a specification of a recipe
step that will perform Pareto scaling on the columns.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
... |
One or more selector functions to choose which
variables are affected by the step. See |
role |
Not used by this step since no new variables are created. |
means |
A named numeric vector of means. This is
|
sdroots |
A named numeric vector of standard deviation square roots. This
is |
na_rm |
A logical value indicating whether |
x |
A |
Pareto scaling is a variant of autoscaling whereby the data is scaled
by the square root of its standard deviation. step_pareto
estimates the standard deviations
and means from the data used in the training
argument of
prep.recipe
. bake.recipe
then applies the scaling to new data sets using
these estimates.
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.
van den Berg, R. A., Hoefsloot, H. C., Westerhuis, J. A., Smilde, A. K., & van der Werf, M. J. (2006). Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC genomics, 7, 142. https://doi.org/10.1186/1471-2164-7-142 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1534033/
1 2 3 4 | # requires the recipes package
pareto <-
recipe(Species ~. , iris) %>%
step_pareto(all_predictors())
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.