Package 'NeuralSTATIS'

Neural STATIS: Multivariate Analysis via Autoencoders for Three-Way Data

Description

Neural STATIS: Multivariate Analysis via Autoencoders for Three-Way Data

Usage

neural_statis(
  data,
  col_tablas,
  col_individuos,
  col_variables = NULL,
  usar_keras = TRUE,
  q_latent = 5,
  hidden_units = c(16),
  epochs = 200,
  batch_size = 32,
  seed = 123
)

Arguments

data	A data frame containing multi-way data.
col_tablas	Character. Column name defining the K tables/time points.
col_individuos	Character. Column name defining the N individuals.
col_variables	Character vector (optional). Selected numerical variables.
usar_keras	Logical. Whether to use Keras autoencoder (TRUE) or fall back to PCA (FALSE).
q_latent	Integer. Number of latent dimensions.
hidden_units	Numeric vector. Architecture of hidden layers.
epochs	Integer. Training epochs for the autoencoder.
batch_size	Integer. Batch size for training.
seed	Integer. Random seed for reproducibility.

Value

An object of class neural_statis.

Examples

# Datos de prueba sintéticos
set.seed(123)
df_test <- data.frame(
  tabla = rep(paste0("T", 1:3), each = 20),
  ind = rep(paste0("I", 1:10), times = 6),
  var1 = rnorm(60),
  var2 = rnorm(60)
)

# Ejemplo de ejecución rápida
res <- neural_statis(
  data = df_test,
  col_tablas = "tabla",
  col_individuos = "ind",
  usar_keras = FALSE,
  q_latent = 2
)

---

Plot Method for Neural STATIS

Description

Plot Method for Neural STATIS

Usage

## S3 method for class 'neural_statis'
plot(x, n_clusters = 4, ...)

Arguments

x	An object of class neural_statis.
n_clusters	Integer. Number of clusters for macro-trajectories (default = 4).
...	Additional graphical parameters.

---