posterior.yodel_bma {yodel}

Posterior Samples from Bayesian Model Averaging

Description

Calculate posterior quantities of interest using Bayesian model averaging.

Usage

## S3 method for class 'yodel_bma'
posterior(x, ...)

Arguments

x

output from a call to bma().

...

additional arguments to be passed to each of the functions used to calculate the quantity of interest.

Value

A dataframe with the posterior samples for each iteration of the MCMC. The dataframe will have, at a minimum, the columns "iter" and "model" indicating the MCMC iteration and the model that was used in the calculations. The functions used for each model are defined within the model_bma() function and used in the bma() function. See the example below.

Examples

# functions which caclulate the dose response for a linear and quadratic model
fun_linear <- function(x, dose) {
  mean_response <- x$b1 + x$b2 * dose
  data.frame(iter = 1:nrow(x), dose = dose, mean = mean_response)
}
fun_quad <- function(x, dose) {
  mean_response <- x$b1 + x$b2 * dose + x$b3 * dose ^ 2
  data.frame(iter = 1:nrow(x), dose = dose, mean = mean_response)
}

# Bayesian model averaging
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,
    fun = fun_linear
  ),
  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,
    fun = fun_quad
  )
)

# posterior samples using Bayesian model averaging
posterior(fit, dose = 1)
posterior(fit, dose = 2)