vignettes/imputation_demo.md
In rMIDAS: Multiple Imputation with Denoising Autoencoders

Imputing missing data using rMIDAS

This vignette provides a brief demonstration of rMIDAS. We show how to use the package to impute missing values in the Adult Census dataset, which is commonly used for benchmarking machine learning tasks.

rMIDAS relies on Python to implement the MIDAS imputation algorithm, so a Python environment needs to be installed on your machine. Currently, Python versions from 3.6 to 3.10 are supported. When the package is first loaded, it will prompt the user on whether to set up a Python environment and its dependencies automatically. Users that choose to set up the environment and dependencies manually, or who use rMIDAS in headless mode can specify a Python binary using set_python_env(). For additional help on manually setting up a Python environment and its dependencies please refer to our other vignettes (Using custom Python versions and Running rMIDAS on a server instance) or visit the rMIDAS GitHub page.

Once rMIDAS is initialized, we can load our data. For the purpose of this example, we’ll use a subset of the Adult data:

library(rMIDAS)

adult <- read.csv("https://raw.githubusercontent.com/MIDASverse/MIDASpy/master/Examples/adult_data.csv",
                  row.names = 1)[1:1000,]

As the dataset has a very low proportion of missingness (one of the reasons it is favored for machine learning tasks), we randomly set 10% of observed values as missing in each column using the rMIDAS’ add_missingness() function:

set.seed(89)

adult <- add_missingness(adult, prop = 0.1)

Next, we make a list of all categorical and binary variables, before preprocessing the data for training using the convert() function. Setting the minmax_scale argument to TRUE ensures that continuous variables are scaled between 0 and 1, which can substantially improve convergence in the training step. All pre-processing steps can be reversed after imputation:

adult_cat <- c('workclass','marital_status','relationship','race','education','occupation','native_country')
adult_bin <- c('sex','class_labels')

# Apply rMIDAS preprocessing steps
adult_conv <- convert(adult, 
                      bin_cols = adult_bin, 
                      cat_cols = adult_cat,
                      minmax_scale = TRUE)

The data are now ready to be fed into the MIDAS algorithm, which involves a single call of the train() function. At this stage, we specify the dimensions, input corruption proportion, and other hyperparameters of the MIDAS neural network as well as the number of training epochs:

# Train the model for 20 epochs
adult_train <- train(adult_conv,
                       training_epochs = 20,
                       layer_structure = c(128,128),
                       input_drop = 0.75,
                       seed = 89)

## Initialising Python connection

Once training is complete, we can generate any number of completed datasets using the complete() function (below we generate 10). The completed dataframes can also be saved as ‘.csv’ files using the file and file_root arguments (not demonstrated here). By default, complete() unscales continuous variables and converts binary and categorical variables back to their original form.

Since the MIDAS algorithm returns predicted probabilities for binary and categorical variables, imputed values of such variables can be generated using one of two options. When fast = FALSE (the default), complete() uses the predicted probabilities for each category level to take a weighted random draw from the set of all levels. When fast = TRUE, the function selects the level with the highest predicted probability. If completed datasets are very large or complete() is taking a long time to run, users may benefit from choosing the latter option:

# Generate 10 imputed datasets
adult_complete <- complete(adult_train, m = 10)

## Imputations generated. Completing post-imputation transformations.

# Inspect first imputed dataset:
head(adult_complete[[1]])

##        age   fnlwgt education_num capital_gain capital_loss hours_per_week
## 1 39.00000  77516.0      10.29917         2174       0.0000             40
## 2 35.19139  83311.0      13.00000            0       0.0000             13
## 3 38.00000 215646.0       9.00000            0       0.0000             40
## 4 53.00000 234721.0       7.00000            0      15.6308             40
## 5 33.40710 338409.0      13.00000            0       0.0000             40
## 6 37.00000 217087.1      14.00000            0       0.0000             40
##      sex class_labels        workclass     marital_status  relationship  race
## 1   Male        <=50K        State-gov      Never-married Not-in-family White
## 2   Male        <=50K Self-emp-not-inc Married-civ-spouse       Husband Black
## 3   Male        <=50K          Private           Divorced Not-in-family White
## 4   Male        <=50K          Private Married-civ-spouse       Husband Black
## 5 Female        <=50K          Private Married-civ-spouse          Wife Black
## 6 Female        <=50K          Private Married-civ-spouse          Wife White
##   education        occupation native_country
## 1 Bachelors      Adm-clerical  United-States
## 2   7th-8th   Exec-managerial  United-States
## 3   HS-grad Handlers-cleaners  United-States
## 4      11th Handlers-cleaners          India
## 5 Bachelors    Prof-specialty           Cuba
## 6   HS-grad   Exec-managerial  United-States

Finally, the combine() function allows users to estimate regression models on the completed datasets with Rubin’s combination rules. This function wraps the glm() package, whose arguments can be used to select different families of estimation methods (gaussian/OLS, binomial etc.) and to specify other aspects of the model:

# Estimate logit model on 10 completed datasets (using Rubin's combination rules)
adult_model <- combine("class_labels ~ hours_per_week + sex", 
                    adult_complete,
                    family = stats::binomial)

adult_model

##             term    estimate  std.error statistic       df      p.value
## 1    (Intercept)  3.60381784 0.37801526  9.533525 153.8703 3.231271e-17
## 2 hours_per_week -0.04751554 0.00834382 -5.694698 135.4073 7.360524e-08
## 3        sexMale -0.73352237 0.18632148 -3.936864 778.4708 8.994749e-05