SRCL_1_initiate_neural_network: Initiates a monotonistc neural network

In ekstroem/SRCL: Synergistic Risk Contributions Learning

Description

This function initiates a monotonistc neural network. The one-hidden layer monotonistic neural network is designed to resemble a DAG with hidden synergistic components. With the model, we intend to learn the various synergistic interactions between the exposures and outcome. The model needs to be monotonistic and estimate the risk on an additive scale. Neural networks include hidden activation functions (if the sum of the input exceeds a threshold, information is passed on), which can model minimum threshold values of interactions between exposures. We need to specify the upper limit of the number of possible hidden activation functions and through model fitting, the model may be able to learn both stand-alone and synergistically interacting factors.

Usage

SRCL_1_initiate_neural_network(inputs, hidden, confounder = FALSE)

Arguments

inputs	The number of exposures.
hidden	Number of hidden nodes.
confounder	Allows to control away a confounder (connected to the output layer)

Details

The monotonistic neural network can be denoted as:

P(Y=1|X^+)=∑_{j}\Big(w_{j,k}^+ReLU_j\big(∑_{i}(w_{i,j}^+X_i^+) + b_j^-\big)\Big) + R^{b}

Examples

#See the example under SRCL_0_synthetic_data

ekstroem/SRCL documentation built on Sept. 5, 2020, 8:59 p.m.