| posterior.beaver_mcmc_bma {beaver} | R Documentation |
Posterior Samples from Bayesian Model Averaging
Description
Calculate posterior quantities of interest using Bayesian model averaging.
Usage
## S3 method for class 'beaver_mcmc_bma'
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"),
...
)
Arguments
x |
an object output from |
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. |
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 columns "iter" and "model", indicating the
MCMC iteration and the model that was used in the calculations, 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 beaver_mcmc() function.
See Also
Other posterior calculations:
beaver_mcmc(),
posterior.beaver_mcmc(),
posterior_g_comp(),
pr_eoi_g_comp(),
pr_eoi()
Examples
# 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")