Data Preprocessor - Transformation Functions — hai_data

Takes in a recipe and will perform the desired transformation on the selected varialbe(s) using a selected recipe. To call the desired transformation recipe use a quoted argument like "boxcos", "bs" etc.

Usage

hai_data_transform(
  .recipe_object = NULL,
  ...,
  .type_of_scale = "log",
  .bc_limits = c(-5, 5),
  .bc_num_unique = 5,
  .bs_deg_free = NULL,
  .bs_degree = 3,
  .log_base = exp(1),
  .log_offset = 0,
  .logit_offset = 0,
  .ns_deg_free = 2,
  .rel_shift = 0,
  .rel_reverse = FALSE,
  .rel_smooth = FALSE,
  .yj_limits = c(-5, 5),
  .yj_num_unique = 5
)

Arguments

.recipe_object

The data that you want to process

...

One or more selector functions to choose variables to be imputed. When used with imp_vars, these dots indicate which variables are used to predict the missing data in each variable. See selections() for more details

.type_of_scale

This is a quoted argument and can be one of the following:

"boxcox"
"bs"
"log"
"logit"
"ns"
"relu"
"sqrt"
"yeojohnson

.bc_limits

A length 2 numeric vector defining the range to compute the transformation parameter lambda.

.bc_num_unique

An integer to specify minimum required unique values to evaluate for a transformation

.bs_deg_free

The degrees of freedom for the spline. As the degrees of freedom for a spline increase, more flexible and complex curves can be generated. When a single degree of freedom is used, the result is a rescaled version of the original data.

.bs_degree

Degree of polynomial spline (integer).

.log_base

A numberic value for the base.

.log_offset

An optional value to add to the data prior to logging (to avoid log(0))

.logit_offset

A numberic value to modify values ofthe columns that are either one or zero. They are modifed to be x - offset or offset respectively.

.ns_deg_free

The degrees of freedom for the natural spline. As the degrees of freedom for a natural spline increase, more flexible and complex curves can be generated. When a single degree of freedom is used, the result is a rescaled version of the original data.

.rel_shift

A numeric value dictating a translation to apply to the data.

.rel_reverse

A logical to indicate if theleft hinge should be used as opposed to the right hinge.

.rel_smooth

A logical indicating if hte softplus function, a smooth approximation to the rectified linear transformation, should be used.

.yj_limits

A length 2 numeric vector defining the range to compute the transformation parameter lambda.

.yj_num_unique

An integer where data that have less possible values will not be evaluated for a transformation.

Value

A list object

Details

This function will get your data ready for processing with many types of ml/ai models.

This is intended to be used inside of the data processor and therefore is an internal function. This documentation exists to explain the process and help the user understand the parameters that can be set in the pre-processor function.

recipes::step_BoxCox()

Author

Steven P. Sanderson II, MPH

Examples

suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(recipes))

date_seq <- seq.Date(from = as.Date("2013-01-01"), length.out = 100, by = "month")
val_seq <- rep(rnorm(10, mean = 6, sd = 2), times = 10)
df_tbl <- tibble(
  date_col = date_seq,
  value    = val_seq
)

rec_obj <- recipe(value ~ ., df_tbl)

hai_data_transform(
  .recipe_object = rec_obj,
  value,
  .type_of_scale = "log"
)$scale_rec_obj %>%
  get_juiced_data()
#> # A tibble: 100 × 2
#>    date_col   value
#>    <date>     <dbl>
#>  1 2013-01-01  1.96
#>  2 2013-02-01  1.93
#>  3 2013-03-01  1.11
#>  4 2013-04-01  1.53
#>  5 2013-05-01  1.79
#>  6 2013-06-01  1.74
#>  7 2013-07-01  1.77
#>  8 2013-08-01  1.60
#>  9 2013-09-01  2.10
#> 10 2013-10-01  2.05
#> # ℹ 90 more rows

Data Preprocessor - Transformation Functions