View source: R/hai-data-impute-missing.R
hai_data_impute | R Documentation |
Takes in a recipe and will impute missing values using a selected recipe. To call the recipe use a quoted argument like "median" or "bagged".
hai_data_impute(
.recipe_object = NULL,
...,
.seed_value = 123,
.type_of_imputation = "mean",
.number_of_trees = 25,
.neighbors = 5,
.mean_trim = 0,
.roll_statistic,
.roll_window = 5
)
.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 |
.seed_value |
To make results reproducible, set the seed. |
.type_of_imputation |
This is a quoted argument and can be one of the following:
|
.number_of_trees |
This is used for the |
.neighbors |
This should be filled in with an integer value if |
.mean_trim |
This should be filled in with a fraction if |
.roll_statistic |
This should be filled in with a single unquoted function
that takes with it a single argument such as mean. This should be filled in
if |
.roll_window |
This should be filled in with an integer value if |
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.
A list object
Steven P. Sanderson II, MPH
https://recipes.tidymodels.org/reference/index.html#section-step-functions-imputation/
step_impute_bag
recipes::step_impute_bag()
https://recipes.tidymodels.org/reference/step_impute_bag.html
step_impute_knn
recipes::step_impute_knn()
https://recipes.tidymodels.org/reference/step_impute_knn.html
step_impute_linear
recipes::step_impute_linear()
https://recipes.tidymodels.org/reference/step_impute_linear.html
step_impute_lower
recipes::step_impute_lower()
https://recipes.tidymodels.org/reference/step_impute_lower.html
step_impute_mean
recipes::step_impute_mean()
https://recipes.tidymodels.org/reference/step_impute_mean.html
step_impute_median
recipes::step_impute_median()
https://recipes.tidymodels.org/reference/step_impute_median.html
step_impute_mode
recipes::step_impute_mode()
https://recipes.tidymodels.org/reference/step_impute_mode.html
step_impute_roll
recipes::step_impute_roll()
https://recipes.tidymodels.org/reference/step_impute_roll.html
Other Data Recipes:
hai_data_poly()
,
hai_data_scale()
,
hai_data_transform()
,
hai_data_trig()
,
pca_your_recipe()
Other Preprocessor:
hai_c50_data_prepper()
,
hai_cubist_data_prepper()
,
hai_data_poly()
,
hai_data_scale()
,
hai_data_transform()
,
hai_data_trig()
,
hai_earth_data_prepper()
,
hai_glmnet_data_prepper()
,
hai_knn_data_prepper()
,
hai_ranger_data_prepper()
,
hai_svm_poly_data_prepper()
,
hai_svm_rbf_data_prepper()
,
hai_xgboost_data_prepper()
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(recipes))
date_seq <- seq.Date(from = as.Date("2013-01-01"), length.out = 100, by = "month")
val_seq <- rep(c(rnorm(9), NA), times = 10)
df_tbl <- tibble(
date_col = date_seq,
value = val_seq
)
rec_obj <- recipe(value ~ ., df_tbl)
hai_data_impute(
.recipe_object = rec_obj,
value,
.type_of_imputation = "roll",
.roll_statistic = median
)$impute_rec_obj %>%
get_juiced_data()
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.