Calculate posterior mean (and quantiles for specific doses for each MCMC iteration of the model.
posterior(
x,
doses,
times,
probs,
reference_dose,
predictive,
return_samples,
iter,
return_stats
)
# S3 method for dreamer_mcmc
posterior(
x,
doses = attr(x, "doses"),
times = attr(x, "times"),
probs = c(0.025, 0.975),
reference_dose = NULL,
predictive = 0,
return_samples = FALSE,
iter = NULL,
return_stats = TRUE
)
# S3 method for dreamer_mcmc_independent
posterior(
x,
doses = attr(x, "doses"),
times = attr(x, "times"),
probs = c(0.025, 0.975),
reference_dose = NULL,
predictive = 0,
return_samples = FALSE,
iter = NULL,
return_stats = TRUE
)
# S3 method for dreamer_bma
posterior(
x,
doses = x$doses,
times = x$times,
probs = c(0.025, 0.975),
reference_dose = NULL,
predictive = 0,
return_samples = FALSE,
iter = NULL,
return_stats = TRUE
)output from a call to dreamer_mcmc.
doses at which to estimate posterior quantities.
a vector of times at which to calculate the posterior response (for longitudinal models only).
quantiles of the posterior to be calculated.
the dose at which to adjust the posterior plot. Specifying a dose returns the plot of pr(trt_dose - trt_reference_dose | data).
An integer. If greater than 0, the return values will
be from the predictive distribution of the mean of predictive
observations.
If 0 (default), the posterior on the dose response mean is returned.
logical indicating if the weighted raw MCMC samples from the Bayesian model averaging used to calculate the mean and quantiles should be returned.
an index on which iterations of the MCMC should be used in the calculations. By default, all MCMC iterations are used.
logical indicating whether or not the posterior statistics should be calculated.
A named list with the following elements:
stats: a tibble the dose, posterior mean, and posterior quantiles.
samps: the weighted posterior samples. Only returned if
return_samples = TRUE.
posterior(dreamer_mcmc): posterior summary for linear model.
posterior(dreamer_mcmc_independent): posterior summary for independent model.
posterior(dreamer_bma): posterior summary for Bayesian model averaging fit.
set.seed(888)
data <- dreamer_data_linear(
n_cohorts = c(20, 20, 20),
dose = c(0, 3, 10),
b1 = 1,
b2 = 3,
sigma = 5
)
# Bayesian model averaging
output <- dreamer_mcmc(
data = data,
n_adapt = 1e3,
n_burn = 1e3,
n_iter = 1e4,
n_chains = 2,
silent = FALSE,
mod_linear = model_linear(
mu_b1 = 0,
sigma_b1 = 1,
mu_b2 = 0,
sigma_b2 = 1,
shape = 1,
rate = .001,
w_prior = 1 / 2
),
mod_quad = model_quad(
mu_b1 = 0,
sigma_b1 = 1,
mu_b2 = 0,
sigma_b2 = 1,
mu_b3 = 0,
sigma_b3 = 1,
shape = 1,
rate = .001,
w_prior = 1 / 2
)
)
#> mod_linear
#> start : 2024-04-01 17:24:44.889
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 6
#> Unobserved stochastic nodes: 3
#> Total graph size: 50
#>
#> Initializing model
#>
#> finish: 2024-04-01 17:24:44.957
#> total : 0.07 secs
#> mod_quad
#> start : 2024-04-01 17:24:44.957
#> Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 6
#> Unobserved stochastic nodes: 4
#> Total graph size: 59
#>
#> Initializing model
#>
#> finish: 2024-04-01 17:24:45.030
#> total : 0.07 secs
posterior(output)
#> $stats
#> # A tibble: 3 × 4
#> dose mean `2.50%` `97.50%`
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0.572 -0.803 1.95
#> 2 3 9.56 7.92 11.0
#> 3 10 30.8 28.6 33.1
#>
# return posterior samples of the mean
post <- posterior(output, return_samples = TRUE)
head(post$samps)
#> iter dose mean_response model
#> 1 1 0 0.6844290 mod_quad
#> 2 6 0 -0.4293629 mod_quad
#> 3 8 0 1.5144070 mod_quad
#> 4 12 0 0.6374851 mod_quad
#> 5 13 0 1.5117433 mod_quad
#> 6 19 0 0.8239574 mod_quad
# from a single model
posterior(output$mod_quad)
#> $stats
#> # A tibble: 3 × 4
#> dose mean `2.50%` `97.50%`
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0.612 -0.808 2.06
#> 2 3 9.37 7.22 11.5
#> 3 10 30.9 28.6 33.3
#>
# posterior of difference of doses
posterior(output, reference_dose = 0)
#> $stats
#> # A tibble: 3 × 5
#> dose reference_dose mean `2.50%` `97.50%`
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 0 0 0
#> 2 3 0 8.98 7.16 10.4
#> 3 10 0 30.3 27.5 33.0
#>