engression: Engression Function

In engression: Engression Modelling

Engression Function

Description

This function fits an engression model to the data. It allows for the tuning of several parameters related to model complexity. Variables are per default internally standardized (predictions are on original scale).

Usage

engression(
  X,
  Y,
  noise_dim = 5,
  hidden_dim = 100,
  num_layer = 3,
  dropout = 0.0,
  batch_norm = FALSE,
  num_epochs = 1000,
  lr = 10^(-4),
  beta = 1,
  silent = FALSE,
  standardize = TRUE,
  output_activation = "identity"
)

Arguments

X	A matrix or data frame representing the predictors.
Y	A matrix or vector representing the target variable(s). If Y is a factor a classification model is fitted (experimental).
noise_dim	The dimension of the noise introduced in the model (default: 5).
hidden_dim	The size of the hidden layer in the model (default: 100).
num_layer	The number of layers in the model (default: 3).
dropout	The dropout rate to be used in the model in case no batch normalization is used. Only active if batch normalization is off. (default: 0.0)
batch_norm	A boolean indicating whether to use batch-normalization (default: FALSE).
num_epochs	The number of epochs to be used in training (default: 1000).
lr	The learning rate to be used in training (default: 10^-4).
beta	The beta scaling factor for energy loss (default: 1).
silent	A boolean indicating whether to suppress output during model training (default: FALSE).
standardize	A boolean indicating whether to standardize the input data (default: TRUE).
output_activation	Specification of the activation function used at the output layer. This value is passed unchanged to engressionfit() where it is validated and interpreted. The default "identity" reproduces linear output. Other options are "tanh", "sigmoid", "relu", "softplus"

Value

An engression model object with class "engression".

Examples

if (torch::torch_is_installed()) {
  n = 1000
  p = 5

  X = matrix(rnorm(n*p),ncol=p)
  Y = (X[,1]+rnorm(n)*0.1)^2 + (X[,2]+rnorm(n)*0.1) + rnorm(n)*0.1
  Xtest = matrix(rnorm(n*p),ncol=p)
  Ytest = (Xtest[,1]+rnorm(n)*0.1)^2 + (Xtest[,2]+rnorm(n)*0.1) + rnorm(n)*0.1

  ## fit engression object
  engr = engression(X,Y)
  print(engr)

  ## prediction on test data
  Yhat = predict(engr,Xtest,type="mean")
  cat("\n correlation between predicted and realized values:  ", signif(cor(Yhat, Ytest),3))
  plot(Yhat, Ytest,xlab="prediction", ylab="observation")

  ## quantile prediction
  Yhatquant = predict(engr,Xtest,type="quantiles")
  ord = order(Yhat)
  matplot(Yhat[ord], Yhatquant[ord,], type="l", col=2,lty=1,xlab="prediction", ylab="observation")
  points(Yhat[ord],Ytest[ord],pch=20,cex=0.5)

  ## sampling from estimated model
  Ysample = predict(engr,Xtest,type="sample",nsample=1)

  ## plot of realized values against first variable
  oldpar <- par()
  par(mfrow=c(1,2))
  plot(Xtest[,1], Ytest, xlab="Variable 1", ylab="Observation")
  ## plot of sampled values against first variable
  plot(Xtest[,1], Ysample, xlab="Variable 1", ylab="Sample from engression model")
  par(oldpar)
}

engression documentation built on April 16, 2026, 9:06 a.m.