Configuration for Statistical Models — configurar

Defines hyperparameters for the specific Bayesian models.

Usage

configurar_prioris(
  nome = NULL,
  mu_priori = NULL,
  sd_mu_priori = NULL,
  omega_eta_priori = NULL,
  nu_priori = NULL,
  sd_nu_priori = NULL,
  omega_zeta_priori = NULL,
  delta_priori = NULL,
  sd_delta_priori = NULL,
  tau_priori = NULL,
  sd_tau_priori = NULL,
  ...
)

Arguments

nome: Name of the model. If NULL, inherits the model used in rodar_agregador().
mu_priori: Prior mean for the latent vote share at \(t=1\). See "Priors Details" section.
sd_mu_priori: Prior uncertainty for the initial latent vote.
omega_eta_priori: Prior mean for the level volatility (\(\omega_\eta\)).
nu_priori: Prior mean for the initial trend (daily growth rate).
sd_nu_priori: Prior uncertainty for the initial trend.
omega_zeta_priori: Prior mean for the trend volatility (\(\omega_\zeta\)).
delta_priori: Mean expected bias for institutes.
sd_delta_priori: Scale of the bias prior.
tau_priori: Mean expected magnitude of errors not explained by sampling or house effects.
sd_tau_priori: Prior uncertainty for non-sampling error.
...: Additional arguments to override default hyperparameters.

Value

A list of model parameters.

Priors Details

These hyperparameters control the strength of assumptions regarding latent state evolution, institute bias, and non-sampling errors.

Variable names refer to the model notation described in https://rnmag.github.io/agregR/index.html#conceptual-framework

State Model - Level (\(\mu\))

mu_priori: Prior mean for the latent vote share at \(t=1\).
sd_mu_priori: Prior uncertainty for the initial latent vote.
- Default values: \(\mu\) starts with a flat prior of N(0.5, 0.5), allowing data to quickly dominate inference.
omega_eta_priori: Prior mean for the level volatility (\(\omega_\eta\)).
- Default values: With omega_eta_priori = 0.002, the model assumes a baseline drift of approx. \(\pm 2\) percentage points over a month (\(1.96 \times \sqrt{30} \times 0.002 \approx 0.02\)).
- Higher values: The latent vote (\(\mu\)) can jump more from one day to the next. The model adapts more quickly to new polls but becomes more "jittery".
- Lower values: The model assumes the public opinion level is more stable over time, resulting in smoother curves.

State Model - Trend (\(\nu\))

nu_priori: Prior mean for the initial trend (daily growth rate).
sd_nu_priori: Prior uncertainty for the initial trend.
- Default values: With nu_priori = 0 and sd_nu_priori = 0.001, the model expects an initial trend within \(\pm 0.2\) percentage points per day (\(1.96 \times 0.001 \approx 0.002\)).
omega_zeta_priori: Prior mean for the trend volatility (\(\omega_\zeta\)).
- Default values: With omega_zeta_priori = 0.00001, the model assumes a linear evolution, allowing the trend to shift rapidly (accelerations) only under strong evidence.
- Higher values: The trend (\(\nu\)) can change direction or magnitude rapidly.
- Lower values: The trend is assumed to be more constant over time (more linear evolution).

Institute Bias (\(\delta\))

delta_priori: Mean expected bias for institutes. Default is 0, except in "Viés Empírico" where it is anchored on past performance.
sd_delta_priori: Scale of the bias prior.
- Default values: With delta_priori = 0 and sd_delta_priori = 0.02, the model assumes that 95% of institutes have a bias within \(\pm 4\) percentage points (\(1.96 \times 0.02 \approx 0.04\)).
- Higher values: Allow for larger, more variable biases across institutes.
- Lower values: Constrain institutes to have similar biases (shrinkage toward the anchor).

Non-Sampling Error (\(\tau\))

tau_priori: Mean expected magnitude of errors not explained by sampling or house effects. In weighted models, this is replaced by the empirical RMSE from past elections.
sd_tau_priori: Prior uncertainty for non-sampling error.
- Default values: With tau_priori = 0.02 and sd_tau_priori = 0.02, the model assumes a baseline of \(\pm 4\) percentage points of "noise" in each poll, allowing it to spread closer to \(\pm 7\) percentage points.
- Higher values: The model treats polls as less precise, widening the credible intervals of the latent state.
- Lower values: The model trusts polling precision more, leading to tighter intervals and potentially more sensitivity to outliers.

Model Defaults

Each model has a specific set of default priors.

Viés Relativo com Pesos (Default)

mu_priori = 0.5, sd_mu_priori = 0.5, omega_eta_priori = 0.002
nu_priori = 0, sd_nu_priori = 0.001, omega_zeta_priori = 0.00001
delta_priori = 0, sd_delta_priori = 0.02
sd_tau_priori = 0.02
Not applicable: tau_priori (model uses empirical values)

Viés Relativo sem Pesos

mu_priori = 0.5, sd_mu_priori = 0.5, omega_eta_priori = 0.002
nu_priori = 0, sd_nu_priori = 0.001, omega_zeta_priori = 0.00001
delta_priori = 0, sd_delta_priori = 0.02
tau_priori = 0.02, sd_tau_priori = 0.02

Viés Empírico

mu_priori = 0.5, sd_mu_priori = 0.5, omega_eta_priori = 0.002
nu_priori = 0, sd_nu_priori = 0.001, omega_zeta_priori = 0.00001
sd_delta_priori = 0.02
sd_tau_priori = 0.02
Not applicable: delta_priori and tau_priori (model uses empirical values for both)

Retrospectivo

mu_priori = 0.5, sd_mu_priori = 0.5, omega_eta_priori = 0.002
nu_priori = 0, sd_nu_priori = 0.001, omega_zeta_priori = 0.00001
delta_priori = 0, sd_delta_priori = 0.02
tau_priori = 0.02, sd_tau_priori = 0.02

Naive

mu_priori = 0.5, sd_mu_priori = 0.5, omega_eta_priori = 0.002
Not applicable: nu_priori, sd_nu_priori, omega_zeta_priori, delta_priori, sd_delta_priori, tau_priori, sd_tau_priori

Examples

# Get default parameters for the "Naive" model
naive_params <- configurar_prioris(nome = "Naive")

# Get parameters for "Naive" and override a default value
custom_params <- configurar_prioris(nome = "Naive", sd_mu_priori = 0.2)