bma {yodel}

Posterior Weights and Model Averaging Setup

Description

Calculate posterior weights of each model and optionally supply MCMC samples and functions (through the bma_model() function) to calculate a quantity of interest from each model using the posterior() function.

Usage

bma(..., seed = sample(.Machine$integer.max, 1))

model_bma_predictive(
  log_post_pred,
  adjustment = 0,
  w_prior = 1,
  mcmc = NULL,
  fun = NULL
)

model_bma_marginal(log_marginal, w_prior = 1, mcmc = NULL, fun = NULL)

Arguments

...

Named calls to the bma_model() function.

seed

an integer which is used to specify the seed when sampling from the different models (e.g. in posterior()).

log_post_pred

a matrix containing the log likelihood for each observation on each iteration of the MCMC. The matrix should have dimensions (number-of-MCMC-iteration) by (number of observations).

adjustment

an adjustment to be applied to the posterior log-predictive likelihood. A simple bias correction in Ando & Tsay (2010) is: - (number of parameters in the model) / 2.

w_prior

the prior weight for the model.

mcmc

a named list (or dataframe) of MCMC samples of model parameters.

fun

a function which takes the MCMC samples and returns a value of interest.

log_marginal

The log marginal likelihood of the model.

Details

It is required that if MCMC samples are supplied, that each MCMC run must have the same number of collected samples.

Value

bma: A list containing the prior and posterior weights for each model, the sampled model (model_index) at each MCMC iteration and the arguments supplied to each bma_model() call.

model_bma: A named list of the arguments, with a "yodel_bma_candidate" class attached.

model_bma: A named list of the arguments, with a "yodel_bma_candidate" class attached.

References

Ando, T., & Tsay, R. (2010). Predictive likelihood for Bayesian model selection and averaging. International Journal of Forecasting, 26(4), 744-763.

Examples

# Minimal example
fit <- bma(
  linear = model_bma_predictive(
    # mcmc = data.frame(b1 = 1:5, b2 = 11:15, sigma = seq(.1, .5, .1)),
    log_post_pred = matrix(log(1:100), 5, 20),
    adjustment = - 3 / 2,
    w_prior = .5
  ),
  quad = model_bma_predictive(
    # mcmc = data.frame(b1 = 1:5 / 2, b2 = 11:15 / 2, b3 = 5:1, sigma = seq(.1, .5, .1)),
    log_post_pred = matrix(log(2:101), 5, 20),
    adjustment = - 4 / 2,
    w_prior = .5
  )
)

fit$w_prior
fit$w_post