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")
)A recipe object. The step will be added to the sequence of operations for this recipe.
One or more selector functions to choose variables for this step.
See selections() for more details.
Not used by this step since no new variables are created.
A logical to indicate if the quantities for preprocessing have been estimated.
A character string of the selected variable names. This field
is a placeholder and will be populated once prep() is used.
A function that takes an ordinal factor vector as an input and outputs a single numeric variable.
A logical. Should the step be skipped when the recipe is baked by
bake()? While all operations are baked when prep() 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.
A character string that is unique to this step to identify it.
An updated version of recipe with the new step added to the
sequence of any existing operations.
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).
When you tidy() this step, a tibble is returned with
columns terms and id:
character, the selectors or variables selected
character, id of this step
The underlying operation does not allow for case weights.
Other dummy variable and encoding steps:
step_bin2factor(),
step_count(),
step_date(),
step_dummy(),
step_dummy_extract(),
step_dummy_multi_choice(),
step_factor2string(),
step_holiday(),
step_indicate_na(),
step_integer(),
step_novel(),
step_num2factor(),
step_other(),
step_regex(),
step_relevel(),
step_string2factor(),
step_time(),
step_unknown(),
step_unorder()
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 -9.073800e-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)
bake(linear_values, new_data = NULL)
#> # A tibble: 3 × 3
#> fail_severity item_paperclip item_twitter
#> <int> <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 × 2
#> terms id
#> <chr> <chr>
#> 1 fail_severity ordinalscore_byijr
nonlin_scores <- prep(nonlin_scores, training = ord_data)
bake(nonlin_scores, new_data = NULL)
#> # A tibble: 3 × 3
#> fail_severity item_paperclip item_twitter
#> <int> <dbl> <dbl>
#> 1 1 1 0
#> 2 3 0 1
#> 3 7 0 0
tidy(nonlin_scores, number = 2)
#> # A tibble: 1 × 2
#> terms id
#> <chr> <chr>
#> 1 fail_severity ordinalscore_byijr