train {rMIDAS}

Train an imputation model using Midas

Description

Build and run a MIDAS neural network on the supplied missing data.

Usage

train(
  data,
  binary_columns = NULL,
  softmax_columns = NULL,
  training_epochs = 10L,
  layer_structure = c(256, 256, 256),
  learn_rate = 4e-04,
  input_drop = 0.8,
  seed = 123L,
  train_batch = 16L,
  latent_space_size = 4,
  cont_adj = 1,
  binary_adj = 1,
  softmax_adj = 1,
  dropout_level = 0.5,
  vae_layer = FALSE,
  vae_alpha = 1,
  vae_sample_var = 1
)

Arguments

data

A data.frame (or coercible) object, or an object of class midas_pre created from rMIDAS::convert()

binary_columns

A vector of column names, containing binary variables. NOTE: if data is a midas_pre object, this argument will be overwritten.

softmax_columns

A list of lists, each internal list corresponding to a single categorical variable and containing names of the one-hot encoded variable names. NOTE: if data is a midas_pre object, this argument will be overwritten.

training_epochs

An integer, indicating the number of forward passes to conduct when running the model.

layer_structure

A vector of integers, The number of nodes in each layer of the network (default = c(256, 256, 256), denoting a three-layer network with 256 nodes per layer). Larger networks can learn more complex data structures but require longer training and are more prone to overfitting.

learn_rate

A number, the learning rate \gamma (default = 0.0001), which controls the size of the weight adjustment in each training epoch. In general, higher values reduce training time at the expense of less accurate results.

input_drop

A number between 0 and 1. The probability of corruption for input columns in training mini-batches (default = 0.8). Higher values increase training time but reduce the risk of overfitting. In our experience, values between 0.7 and 0.95 deliver the best performance.

seed

An integer, the value to which Python's pseudo-random number generator is initialized. This enables users to ensure that data shuffling, weight and bias initialization, and missingness indicator vectors are reproducible.

train_batch

An integer, the number of observations in training mini-batches (default = 16).

latent_space_size

An integer, the number of normal dimensions used to parameterize the latent space.

cont_adj

A number, weights the importance of continuous variables in the loss function

binary_adj

A number, weights the importance of binary variables in the loss function

softmax_adj

A number, weights the importance of categorical variables in the loss function

dropout_level

A number between 0 and 1, determines the number of nodes dropped to "thin" the network

vae_layer

Boolean, specifies whether to include a variational autoencoder layer in the network

vae_alpha

A number, the strength of the prior imposed on the Kullback-Leibler divergence term in the variational autoencoder loss functions.

vae_sample_var

A number, the sampling variance of the normal distributions used to parameterize the latent space.

Details

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

Value

Object of class midas from which completed datasets can be drawn, using rMIDAS::complete()

References

Lall R, Robinson T (2023). “Efficient Multiple Imputation for Diverse Data in Python and R: MIDASpy and rMIDAS.” Journal of Statistical Software, 107(9), 1–38. doi:10.18637/jss.v107.i09.

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)