estimate_BIN {BINtools}

Estimate a BIN (Bias, Information, Noise) model

Description

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.

Usage

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
)

Arguments

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:NULL) List of vectors containing the predictions made for each event by forecasters in the treatment group. The j-th vector contains predictions for the j-th event.

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: 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

The number of initial iterations used for “burnin.” These values are not included in the analysis of the model. (Default:2000)

iter

Total number of iterations. Must be larger than warmup. (Default:4000)

seed

(Default: 1)

Value

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.

See Also

simulate_data, complete_summary

Examples

## 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)