posterior | R Documentation |
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 class '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 class '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 class '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 )
x |
output from a call to |
doses |
doses at which to estimate posterior quantities. |
times |
a vector of times at which to calculate the posterior response (for longitudinal models only). |
probs |
quantiles of the posterior to be calculated. |
reference_dose |
the dose at which to adjust the posterior plot. Specifying a dose returns the plot of pr(trt_dose - trt_reference_dose | data). |
predictive |
An integer. If greater than 0, the return values will
be from the predictive distribution of the mean of |
return_samples |
logical indicating if the weighted raw MCMC samples from the Bayesian model averaging used to calculate the mean and quantiles should be returned. |
iter |
an index on which iterations of the MCMC should be used in the calculations. By default, all MCMC iterations are used. |
return_stats |
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 ) ) posterior(output) # return posterior samples of the mean post <- posterior(output, return_samples = TRUE) head(post$samps) # from a single model posterior(output$mod_quad) # posterior of difference of doses posterior(output, reference_dose = 0)
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