DeepLearningCausal (version 0.0.107)

neuralnet_response_model: 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
)

Value

trained response model object

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.