neuralnet_response_model: Modeling Responses from experimental data Using Deep NN

In DeepLearningCausal: Causal Inference with Super Learner and Deep Neural Networks

Modeling Responses from experimental data Using Deep NN

Description

Model Responses from all compliers (actual + predicted) in experimental data using neural network.

Usage

neuralnet_response_model(
  response.formula,
  exp.data,
  neuralnet.compliers,
  compl.var,
  algorithm = "rprop+",
  hidden.layer = c(4, 2),
  act.fct = "logistic",
  err.fct = "sse",
  linear.output = TRUE,
  stepmax = 1e+08
)

Arguments

response.formula	formula for response variable and covariates (y ~ x)
exp.data	data.frame of experimental data.
neuralnet.compliers	data.frame of compliers (actual + predicted) from neuralnet_predict.
compl.var	string of compliance variable
algorithm	neural network algorithm, default set to "rprop+".
hidden.layer	vector specifying hidden layers and number of neurons.
act.fct	"logistic" or "tanh activation function.
err.fct	"sse" for sum of squared errors or "ce" for cross-entropy.
linear.output	logical for whether output (outcome variable) is linear or not.
stepmax	maximum number of steps for training model.

Value

trained response model object

DeepLearningCausal documentation built on Nov. 6, 2025, 5:08 p.m.