complete: Impute missing values using imputation model

In rMIDAS: Multiple Imputation with Denoising Autoencoders

Impute missing values using imputation model

Description

Having trained an imputation model, complete() produces m completed datasets, saved as a list.

Usage

complete(
  mid_obj,
  m = 10L,
  unscale = TRUE,
  bin_label = TRUE,
  cat_coalesce = TRUE,
  fast = FALSE,
  file = NULL,
  file_root = NULL
)

Arguments

mid_obj	Object of class midas, the result of running rMIDAS::train()
m	An integer, the number of completed datasets required
unscale	Boolean, indicating whether to unscale any columns that were previously minmax scaled between 0 and 1
bin_label	Boolean, indicating whether to add back labels for binary columns
cat_coalesce	Boolean, indicating whether to decode the one-hot encoded categorical variables
fast	Boolean, indicating whether to impute category with highest predicted probability (TRUE), or to use predicted probabilities to make weighted sample of category levels (FALSE)
file	Path to save completed datasets. If NULL, completed datasets are only loaded into memory.
file_root	A character string, used as the root for all filenames when saving completed datasets if a filepath is supplied. If no file_root is provided, completed datasets will be saved as "file/midas_impute_yymmdd_hhmmss_m.csv"

Details

For more information, see Lall and Robinson (2023): doi:10.18637/jss.v107.i09.

Value

List of length m, each element of which is a completed data.frame (i.e. no missing values)

References

\insertRef

rmidas_jssrMIDAS

Examples

# Generate raw data, with numeric, binary, and categorical variables
## Not run: 
# Run where Python available and configured correctly
if (python_configured()) {
set.seed(89)
n_obs <- 10000
raw_data <- data.table(a = sample(c("red","yellow","blue",NA),n_obs, replace = TRUE),
                       b = 1:n_obs,
                       c = sample(c("YES","NO",NA),n_obs,replace=TRUE),
                       d = runif(n_obs,1,10),
                       e = sample(c("YES","NO"), n_obs, replace = TRUE),
                       f = sample(c("male","female","trans","other",NA), n_obs, replace = TRUE))

# Names of bin./cat. variables
test_bin <- c("c","e")
test_cat <- c("a","f")

# Pre-process data
test_data <- convert(raw_data,
                     bin_cols = test_bin,
                     cat_cols = test_cat,
                     minmax_scale = TRUE)

# Run imputations
test_imp <- train(test_data)

# Generate datasets
complete_datasets <- complete(test_imp, m = 5, fast = FALSE)

# Use Rubin's rules to combine m regression models
midas_pool <- combine(formula = d~a+c+e+f,
                      complete_datasets)
}

## End(Not run)

rMIDAS documentation built on Oct. 11, 2023, 5:14 p.m.