step_ordinalscore creates a specification of a recipe step that will convert ordinal factor variables into numeric scores.

step_ordinalscore(recipe, ..., role = NA, trained = FALSE,
  columns = NULL, convert = as.numeric, skip = FALSE,
  id = rand_id("ordinalscore"))

# S3 method for step_ordinalscore
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.

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.

columns

A character string of variables that will be converted. This is NULL until computed by prep.recipe().

convert

A function that takes an ordinal factor vector as an input and outputs a single numeric variable.

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_ordinalscore 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 columns that will be affected).

Details

Dummy variables from ordered factors with C levels will create polynomial basis functions with C-1 terms. As an alternative, this step can be used to translate the ordered levels into a single numeric vector of values that represent (subjective) scores. By default, the translation uses a linear scale (1, 2, 3, ... C) but custom score functions can also be used (see the example below).

Examples

fail_lvls <- c("meh", "annoying", "really_bad") ord_data <- data.frame(item = c("paperclip", "twitter", "airbag"), fail_severity = factor(fail_lvls, levels = fail_lvls, ordered = TRUE)) model.matrix(~fail_severity, data = ord_data)
#> (Intercept) fail_severity.L fail_severity.Q #> 1 1 -7.071068e-01 0.4082483 #> 2 1 -7.850462e-17 -0.8164966 #> 3 1 7.071068e-01 0.4082483 #> attr(,"assign") #> [1] 0 1 1 #> attr(,"contrasts") #> attr(,"contrasts")$fail_severity #> [1] "contr.poly" #>
linear_values <- recipe(~ item + fail_severity, data = ord_data) %>% step_dummy(item) %>% step_ordinalscore(fail_severity) linear_values <- prep(linear_values, training = ord_data, retain = TRUE) juice(linear_values, everything())
#> # A tibble: 3 x 3 #> fail_severity item_paperclip item_twitter #> <dbl> <dbl> <dbl> #> 1 1 1 0 #> 2 2 0 1 #> 3 3 0 0
custom <- function(x) { new_values <- c(1, 3, 7) new_values[as.numeric(x)] } nonlin_scores <- recipe(~ item + fail_severity, data = ord_data) %>% step_dummy(item) %>% step_ordinalscore(fail_severity, convert = custom) tidy(nonlin_scores, number = 2)
#> # A tibble: 1 x 2 #> terms id #> <chr> <chr> #> 1 fail_severity ordinalscore_qIB0v
nonlin_scores <- prep(nonlin_scores, training = ord_data, retain = TRUE) juice(nonlin_scores, everything())
#> # A tibble: 3 x 3 #> fail_severity item_paperclip item_twitter #> <dbl> <dbl> <dbl> #> 1 1 1 0 #> 2 3 0 1 #> 3 7 0 0
tidy(nonlin_scores, number = 2)
#> # A tibble: 1 x 2 #> terms id #> <chr> <chr> #> 1 fail_severity ordinalscore_qIB0v