beaver (version 1.0.0)

posterior.beaver_mcmc: Posterior Samples from Bayesian Model Averaging

Description

Calculate posterior quantities of interest using Bayesian model averaging.

Usage

# S3 method for beaver_mcmc
posterior(
  x,
  doses = attr(x, "doses"),
  reference_dose = NULL,
  prob = c(0.025, 0.975),
  return_stats = TRUE,
  return_samples = FALSE,
  new_data = NULL,
  contrast = NULL,
  reference_type = c("difference", "ratio"),
  ...
)

Value

A list with the elements stats and samples. When using this function with default settings, samples is NULL and stats is a dataframe summarizing the posterior samples. stats contains, at a minimum, the columns "dose", ".contrast_index", "(Intercept)", "value", and variables corresponding to the values passed in prob ("2.50%" and "97.50%" by default). When return_stats is set to FALSE, stats is NULL. When return_samples is set to TRUE, samples is a dataframe with the posterior samples for each iteration of the MCMC. The dataframe will have, at a minimum, the column "iter", indicating the MCMC iteration, as well as the columns "dose", ".contrast_index", "(Intercept)", and "value". The functions used for each model are defined within the model_negbin_XYZ() functions and used in the run_mcmc() function.

Arguments

x

an object output from (internal function) run_mcmc().

doses

doses at which to obtain the posterior.

reference_dose

dose to which to compare as either a difference or ratio.

prob

the percentiles of the posterior to calculate for each dose.

return_stats

logical indicating if the posterior mean and quantiles should be returned.

return_samples

logical indicating if posterior mean samples should be returned.

new_data

a dataframe for which the posterior will be calculated for each observation's covariate values.

contrast

a matrix containing where each row contains a contrast for which the posterior will be calculated.

reference_type

whether to provide the posterior of the difference or the ratio between each dose and the reference dose.

...

additional arguments will throw an error.

See Also

Other posterior calculations: beaver_mcmc(), posterior.beaver_mcmc_bma(), posterior_g_comp(), pr_eoi_g_comp(), pr_eoi()

Examples

# \donttest{
# The {beaver} package, by definition, performs MCMC for multiple models.
# Even with a small number of chains/burn-ins/samples, a minimally illustrative
# example requires >5s to run.

library(dplyr)

# No covariates----

set.seed(100)

df <- data_negbin_emax(
  n_per_arm = 10,
  doses = 0:3,
  b1 = 0,
  b2 = 2.5,
  b3 = 0.5,
  ps = 0.75
)

df %>%
  group_by(dose) %>%
  summarize(
    mean = mean(response),
    se = sd(response) / sqrt(n()),
    .groups = "drop"
  )

mcmc <- beaver_mcmc(
  emax = model_negbin_emax(
    mu_b1 = 0,
    sigma_b1 = 10,
    mu_b2 = 0,
    sigma_b2 = 10,
    mu_b3 = 1.5,
    sigma_b3 = 3,
    w_prior = 1 / 4
  ),
  linear = model_negbin_linear(
    mu_b1 = 0,
    sigma_b1 = 10,
    mu_b2 = 0,
    sigma_b2 = 10,
    w_prior = 1 / 4
  ),
  quad = model_negbin_quad(
    mu_b1 = 0,
    sigma_b1 = 10,
    mu_b2 = 0,
    sigma_b2 = 10,
    mu_b3 = 1.5,
    sigma_b3 = 3,
    w_prior = 1 / 4
  ),
  exp = model_negbin_exp(
    mu_b1 = 0,
    sigma_b1 = 10,
    mu_b2 = 0,
    sigma_b2 = 10,
    mu_b3 = 0,
    sigma_b3 = 3,
    w_prior = 1 / 4
  ),
  formula = ~ 1,
  data = df,
  n_iter = 1e2,
  n_chains = 1,
  quiet = TRUE
)

mcmc$w_post

draws <- try(draws(mcmc)) #draws() is intended for single model fits only
draws_emax <- draws(mcmc$models$emax$mcmc)
draws_linear <- draws(mcmc$models$linear$mcmc)
draws_quad <- draws(mcmc$models$quad$mcmc)
draws_exp <- draws(mcmc$models$exp$mcmc)

post <- posterior(
  mcmc,
  contrast = matrix(1, 1, 1),
  doses = 0:3,
  reference_dose = 0,
  reference_type = "difference"
)

pr_eoi(
  mcmc,
  eoi = c(5, 8),
  contrast = matrix(1, 1, 1),
  reference_dose = 0,
  reference_type = "difference"
)

post_g_comp <- posterior_g_comp(
  mcmc,
  new_data = df,
  reference_dose = 0,
  reference_type = "difference"
)

pr_eoi_g_comp(
  mcmc,
  eoi = c(5, 8),
  new_data = df,
  reference_dose = 0,
  reference_type = "difference"
)

plot(mcmc, contrast = matrix(1, 1, 1))

# With covariates----

set.seed(1000)

x <-
  data.frame(
    gender = factor(sample(c("F", "M"), 40, replace = TRUE))
  ) %>%
  model.matrix(~ gender, data = .)

df_cov <-
  data_negbin_emax(
    n_per_arm = 10,
    doses = 0:3,
    b1 = c(0, 0.5),
    b2 = 2.5,
    b3 = 0.5,
    ps = 0.75,
    x = x
  ) %>%
  mutate(
    gender = case_when(
      genderM == 1 ~ "M",
      TRUE ~ "F"
    ),
    gender = factor(gender)
  ) %>%
  select(subject, dose, gender, response)

df_cov %>%
  group_by(dose, gender) %>%
  summarize(
    mean = mean(response),
    se = sd(response) / sqrt(n()),
    .groups = "drop"
  )

mcmc_cov <- beaver_mcmc(
  emax = model_negbin_emax(
    mu_b1 = 0,
    sigma_b1 = 10,
    mu_b2 = 0,
    sigma_b2 = 10,
    mu_b3 = 1.5,
    sigma_b3 = 3,
    w_prior = 1 / 4
  ),
  linear = model_negbin_linear(
    mu_b1 = 0,
    sigma_b1 = 10,
    mu_b2 = 0,
    sigma_b2 = 10,
    w_prior = 1 / 4
  ),
  quad = model_negbin_quad(
    mu_b1 = 0,
    sigma_b1 = 10,
    mu_b2 = 0,
    sigma_b2 = 10,
    mu_b3 = 1.5,
    sigma_b3 = 3,
    w_prior = 1 / 4
  ),
  exp = model_negbin_exp(
    mu_b1 = 0,
    sigma_b1 = 10,
    mu_b2 = 0,
    sigma_b2 = 10,
    mu_b3 = 0,
    sigma_b3 = 3,
    w_prior = 1 / 4
  ),
  formula = ~ gender,
  data = df_cov,
  n_iter = 1e2,
  n_chains = 1,
  quiet = TRUE
)

mcmc_cov$w_post

draws_cov <- try(draws(mcmc_cov)) #draws() is intended for single model fits only
draws_cov_emax <- draws(mcmc_cov$models$emax$mcmc)
draws_cov_linear <- draws(mcmc_cov$models$linear$mcmc)
draws_cov_quad <- draws(mcmc_cov$models$quad$mcmc)
draws_cov_exp <- draws(mcmc_cov$models$exp$mcmc)

post_cov <- posterior(
  mcmc_cov,
  contrast = matrix(c(1, 1, 0, 1), 2, 2),
  doses = 0:3,
  reference_dose = 0,
  reference_type = "difference"
)

pr_eoi(
  mcmc_cov,
  eoi = c(5, 8),
  contrast = matrix(c(1, 1, 0, 1), 2, 2),
  reference_dose = 0,
  reference_type = "difference"
)

post_g_comp_cov <- posterior_g_comp(
  mcmc_cov,
  new_data = df_cov,
  reference_dose = 0,
  reference_type = "difference"
)

pr_eoi_g_comp(
  mcmc_cov,
  eoi = c(5, 8),
  new_data = df_cov,
  reference_dose = 0,
  reference_type = "difference"
)

plot(mcmc_cov, new_data = df_cov, type = "g-comp")
# }