post_medx | R Documentation |
Posterior Distribution of Minimum X% Effective Dose
post_medx( x, ed, probs, time, lower, upper, greater, small_bound, return_samples, ... ) ## S3 method for class 'dreamer_bma' post_medx( x, ed, probs = c(0.025, 0.975), time = NULL, lower = min(x$doses), upper = max(x$doses), greater = TRUE, small_bound = 0, return_samples = FALSE, ... ) ## S3 method for class 'dreamer_mcmc' post_medx( x, ed, probs = c(0.025, 0.975), time = NULL, lower = min(attr(x, "doses")), upper = max(attr(x, "doses")), greater = TRUE, small_bound = 0, return_samples = FALSE, index = 1:(nrow(x[[1]]) * length(x)), ... )
x |
output from |
ed |
a number between 0 and 100 indicating the ed% dose that is being sought. |
probs |
a vector of quantiles to calculate on the posterior. |
time |
the slice of time for which to calculate the posterior EDX dose. Applies to longitudinal models only. |
lower |
the lower bound of the doses for calculating EDX. |
upper |
the upper bound of the doses for calculating EDX. |
greater |
if |
small_bound |
the minimum ( |
return_samples |
logical indicating if the posterior samples should be returned. |
... |
additional arguments for specific methods. |
index |
a vector indicating which MCMC samples to use in the
calculation. If |
The minimum X% effective dose is the dose that has X% of the
largest effect for doses between lower
and upper
. When greater
is TRUE
, larger positive responses are considered more effective and
vice versa. The X% response is calculated as small_bound
+
ed
/ 100 * (max_effect - small_bound
) where "max_effect" is the
maximum response for doses between lower
and upper
. The X% effective
dose is the smallest dose which has X% response within the dose range.
It is possible that for some MCMC samples, an X% effective dose may
not exist, so probabilities are not guaranteed to sum to one.
Posterior quantities and samples (if applicable),
generally in the form of a list. The pr_edx_exists
column gives the
posterior probability that an EDX% effect exists.
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 = 1e3, 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 ) ) post_medx(output, ed = c(50, 90)) # from a single model post_medx(output$mod_linear, ed = c(50, 90))
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