
step_impute_lower
creates a specification of a recipe step
designed for cases where the non-negative numeric data cannot be
measured below a known value. In these cases, one method for
imputing the data is to substitute the truncated value by a
random uniform number between zero and the truncation point.
step_impute_lower(
recipe,
...,
role = NA,
trained = FALSE,
threshold = NULL,
skip = FALSE,
id = rand_id("impute_lower")
)step_lowerimpute(
recipe,
...,
role = NA,
trained = FALSE,
threshold = NULL,
skip = FALSE,
id = rand_id("impute_lower")
)
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 named numeric vector of lower bounds. This is
NULL
until computed by prep()
.
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.
When you tidy()
this step, a tibble with columns
terms
(the selectors or variables selected) and value
for the
estimated threshold is returned.
step_impute_lower
estimates the variable minimums
from the data used in the training
argument of prep.recipe
.
bake.recipe
then simulates a value for any data at the minimum
with a random uniform value between zero and the minimum.
As of recipes
0.1.16, this function name changed from step_lowerimpute()
to step_impute_lower()
.
Other imputation steps:
step_impute_bag()
,
step_impute_knn()
,
step_impute_linear()
,
step_impute_mean()
,
step_impute_median()
,
step_impute_mode()
,
step_impute_roll()
# NOT RUN {
library(recipes)
library(modeldata)
data(biomass)
## Truncate some values to emulate what a lower limit of
## the measurement system might look like
biomass$carbon <- ifelse(biomass$carbon > 40, biomass$carbon, 40)
biomass$hydrogen <- ifelse(biomass$hydrogen > 5, biomass$carbon, 5)
biomass_tr <- biomass[biomass$dataset == "Training",]
biomass_te <- biomass[biomass$dataset == "Testing",]
rec <- recipe(HHV ~ carbon + hydrogen + oxygen + nitrogen + sulfur,
data = biomass_tr)
impute_rec <- rec %>%
step_impute_lower(carbon, hydrogen)
tidy(impute_rec, number = 1)
impute_rec <- prep(impute_rec, training = biomass_tr)
tidy(impute_rec, number = 1)
transformed_te <- bake(impute_rec, biomass_te)
plot(transformed_te$carbon, biomass_te$carbon,
ylab = "pre-imputation", xlab = "imputed")
# }
Run the code above in your browser using DataLab