neuralnet_complier_mod: Train compliance model using neural networks

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

Train compliance model using neural networks

Description

Train model using group exposed to treatment with compliance as binary outcome variable and covariates.

Usage

neuralnet_complier_mod(
  complier.formula,
  exp.data,
  treat.var,
  algorithm = "rprop+",
  hidden.layer = c(4, 2),
  ID = NULL,
  stepmax = 1e+08
)

Arguments

complier.formula	formula for complier variable as outcome and covariates (c ~ x)
exp.data	data.frame for experimental data.
treat.var	string for treatment variable.
algorithm	string for algorithm for training neural networks. Default set to the Resilient back propagation with weight backtracking (rprop+). Other algorithms include backprop', rprop-', 'sag', or 'slr' (see neuralnet package).
hidden.layer	vector for specifying hidden layers and number of neurons.
ID	string for identifier variable
stepmax	maximum number of steps.

Value

trained complier model object

DeepLearningCausal documentation built on Sept. 11, 2024, 8:40 p.m.