Description Usage Arguments Value Examples

View source: R/sample_aggregator.R

This function allows the user to compute the revealed aggregator from *Satopää, V.A. (2021):
Regularized Aggregation of One-off Probability Predictions*.
The current version of the paper is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3769945.

1 2 3 4 5 6 7 8 9 10 11 12 | ```
sample_aggregator(
p,
p0 = NULL,
alpha = NULL,
beta = NULL,
a = 1/2,
b = 1/2,
num_sample = 1e+06,
burnin = num_sample/2,
thin = 1,
seed = 1
)
``` |

`p` |
Vector of |

`p0` |
The forecasters' common prior. This represents a probability prediction based on some of the forecasters' common evidence and must be strictly between 0 and 1. |

`alpha, beta` |
The shape and scale parameters of the prior beta distribution of the common prior.
If omitted, the sampler uses the fixed common prior given by |

`a, b` |
The parameters for the prior distribution of |

`num_sample` |
The number of posterior samples to be drawn. This does not take into account burnin and thinning. |

`burnin` |
The number of the initial |

`thin` |
After |

`seed` |
The seed value for random value generation. |

A data frame with rows representing posterior draws of *(p*, ρ, γ, δ, p0)*. The columns are:

`aggregate`

: The posterior samples of the oracle aggregator*p**. The average of these values gives the revealed aggregator*p''*. The 95% interval of these values gives the 95% credible interval of the oracle aggregator.`rho`

: The posterior samples of the forecasters' shared evidence,*ρ*.`gamma`

: The posterior samples of the forecasters' total evidence,*γ*. The difference`gamma`

-`rho`

gives the posterior samples of the forecasters' rational disagreement.`delta`

: The posterior samples of the forecasters' total evidence plus noise,*δ*. The difference`delta`

-`gamma`

gives the posterior samples of the forecasters' irrational disagreement.`p0`

: The posterior samples of the forecasters' common prior. If a beta prior distribution is not specified via the arguments`alpha`

and`beta`

, then all elements of this column are equal to the fixed common prior given by the`p0`

argument.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
# Illustration on Scenario B in Satopää, V.A. (2021).
# Forecasters' probability predictions:
p = c(1/2, 5/16, 1/8, 1/4, 1/2)
# Aggregate with a fixed common prior of 0.5.
# Sample the posterior distribution:
post_sample = sample_aggregator(p, p0 = 0.5, num_sample = 10^6, seed = 1)
# The posterior means of the model parameters:
colMeans(post_sample[,-1])
# The posterior mean of the oracle aggregator, a.k.a., the revealed aggregator:
mean(post_sample[,1])
# The 95% credible interval for the oracle aggregator:
quantile(post_sample[,1], c(0.025, 0.975))
# Aggregate based a uniform distribution on the common prior
# Recall that Beta(1,1) corresponds to the uniform distribution.
# Sample the posterior distribution:
post_sample = sample_aggregator(p, alpha = 1, beta = 1, num_sample = 10^6, seed = 1)
# The posterior means of the oracle aggregate and the model parameters:
colMeans(post_sample)
``` |

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