multiple imputation through variational autoencoders (latent) haven't change default setting yet

Usage

mivae.latent(
  data,
  m = 5,
  categorical.encoding = "embeddings",
  device = "cpu",
  epochs = 5,
  batch.size = 32,
  subsample = 1,
  early.stopping.epochs = 1,
  vae.params = list(),
  pmm.params = list(),
  loss.na.scale = FALSE,
  verbose = TRUE,
  print.every.n = 1,
  save.model = FALSE,
  path = NULL
)

Arguments

data: A data frame, tibble or data table with missing values.
m: The number of imputed datasets.
categorical.encoding: The method for representing multi-class categorical features. Can be either "embeddings" or "onehot" (default).
device: Device to use. Either "cpu" (default) or "cuda" for GPU.
epochs: The number of training epochs (iterations). Default: 100.
batch.size: The size of samples in each batch. Default: 512.
subsample: The subsample ratio of training data. Default: 1.
early.stopping.epochs: An integer value k. Mivae training will stop if the validation performance has not improved for k epochs, only used when subsample<1. Default: 1.
vae.params: A list of parameters for variational autoencoders. See vae_default for details.
pmm.params: A list of parameters for predictive mean matching. See vae_pmm_default for details.
loss.na.scale: Whether to multiply the ratio of missing values in a feature to calculate the loss function. Default: FALSE.
verbose: Whether or not to print training loss information. Default: TRUE.
print.every.n: If verbose is set to TRUE, print out training loss for every n epochs. Default: 1.
save.model: Whether or not to save the imputation model. Default: FALSE.
path: The path where the final imputation model will be saved.