CoOL_2_train_neural_network {CoOL} | R Documentation |

This function trains the non-negative neural network. Fitting the model is done in a step-wise procedure one individual at a time, where the model estimates individual's risk of the disease outcome, estimates the prediction's residual error and adjusts the model parameters to reduce this error. By iterating through all individuals for multiple epochs (one complete iterations through all individuals is called an epoch), we end with parameters for the model, where the errors are smallest possible for the full population. The model fit follows the linear expectation that synergism is a combined effect larger than the sum of independent effects. The initial values, derivatives, and learning rates are described in further detail in the Supplementary material. The non-negative model ensures that the predicted value cannot be negative. The model does not prevent estimating probabilities above 1, but this would be unlikely, as risks of disease and mortality even for high risk groups in general are far below 1. The use of a test dataset does not seem to assist deciding on the optimal number of epochs possibly due to the constrains due to the non-negative assumption. We suggest splitting data into a train and test data set, such that findings from the train data set can be confirmed in the test data set before developing hypotheses.

CoOL_2_train_neural_network( X_train, Y_train, X_test, Y_test, C_train = 0, C_test = 0, model, lr = c(1e-04, 1e-05, 1e-06), epochs = 2000, patience = 100, monitor = TRUE, plot_and_evaluation_frequency = 50, input_parameter_reg = 0.001, spline_df = 10, restore_par_options = TRUE, drop_out = 0, fix_baseline_risk = -1, ipw = 1 )

`X_train` |
The exposure data for the training data. |

`Y_train` |
The outcome data for the training data. |

`X_test` |
The exposure data for the test data (currently the training data is used). |

`Y_test` |
The outcome data for the test data (currently the training data is used). |

`C_train` |
One variable to adjust the analysis for such as calendar time (training data). |

`C_test` |
One variable to adjust the analysis for such as calendar time (currently the training data is used). |

`model` |
The fitted non-negative neural network. |

`lr` |
Learning rate (several LR can be provided, such that the model training will train for each LR and continue to the next). |

`epochs` |
Epochs. |

`patience` |
The number of epochs allowed without an improvement in performance. |

`monitor` |
Whether a monitoring plot will be shown during training. |

`plot_and_evaluation_frequency` |
The interval for plotting the performance and checking the patience. |

`input_parameter_reg` |
Regularisation decreasing parameter value at each iteration for the input parameters. |

`spline_df` |
Degrees of freedom for the spline fit for the performance plots. |

`restore_par_options` |
Restore par options. |

`drop_out` |
To drop connections if their weights reaches zero. |

`fix_baseline_risk` |
To fix the baseline risk at a value. |

`ipw` |
a vector of weights per observation to allow for inverse probability of censoring weighting to correct for selection bias |

An updated list of connection weights, bias weights and meta data.

Rieckmann, Dworzynski, Arras, Lapuschkin, Samek, Arah, Rod, Ekstrom. Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. medRxiv (2020) <doi:10.1101/2020.12.10.20225243>

#See the example under CoOL_0_working_example

[Package *CoOL* version 1.0.3 Index]