estimate_BIN {BINtools} | R Documentation |

This function allows the user to compare two groups (treatment and control) of forecasters in terms of their bias, information, and noise levels. Model estimation is performed with a Markov Chain Monte Carlo (MCMC) approach called Hamiltonian Monte Carlo.

estimate_BIN( Outcomes, Control, Treatment = NULL, initial = list(mu_star = 0, mu_0 = 0, mu_1 = 0, gamma_0 = 0.4, gamma_1 = 0.4, delta_0 = 0.5, rho_0 = 0.27, delta_1 = 0.5, rho_1 = 0.27, rho_01 = 0.1), warmup = 2000, iter = 4000, seed = 1 )

`Outcomes` |
Vector of binary values indicating the outcome of each event. The j-th entry is equal to 1 if the j-th event occurs and equal to 0 otherwise. |

`Control` |
List of vectors containing the predictions made for each event by forecasters in the control group. The j-th vector contains predictions for the j-th event. |

`Treatment` |
(Default: |

`initial` |
A list containing the initial values for the parameters mu_star,mu_0,mu_1,gamma_0,gamma_1,delta_0,rho_0,delta_1,rho_1,and rho_01.
(Default: |

`warmup` |
The number of initial iterations used for “burnin.”
These values are not included in the analysis of the model. (Default: |

`iter` |
Total number of iterations.
Must be larger than warmup. (Default: |

`seed` |
(Default: |

Model estimation is performed with the statistical programming language called Stan. The return object is a Stan model. This way the user can apply available diagnostics tools in other packages, such as rstan, to analyze the final results.

`simulate_data`

, `complete_summary`

## An example with one group # a) Simulate synthetic data: synthetic_data = simulate_data(list(mu_star = -0.8,mu_0 = -0.5,mu_1 = 0.2,gamma_0 = 0.1, gamma_1 = 0.3,rho_0 = 0.05,delta_0 = 0.1,rho_1 = 0.2, delta_1 = 0.3,rho_01 = 0.05),300,100,0) # b) Estimate the BIN-model on the synthetic data: full_bayesian_fit = estimate_BIN(synthetic_data$Outcomes,synthetic_data$Control, warmup = 500, iter = 1000) # c) Analyze the results: complete_summary(full_bayesian_fit) ## An example with two groups # a) Simulate synthetic data: synthetic_data = simulate_data(list(mu_star = -0.8,mu_0 = -0.5,mu_1 = 0.2,gamma_0 = 0.1, gamma_1 = 0.3, rho_0 = 0.05,delta_0 = 0.1, rho_1 = 0.2, delta_1 = 0.3,rho_01 = 0.05), 300,100,100) # b) Estimate the BIN-model on the synthetic data: full_bayesian_fit = estimate_BIN(synthetic_data$Outcomes,synthetic_data$Control, synthetic_data$Treatment,warmup = 500, iter = 1000) # c) Analyze the results: complete_summary(full_bayesian_fit)

[Package *BINtools* version 0.1.0 Index]