Performs Bayesian MCP Test step and modeling in a combined fashion. See performBayesianMCP() function for MCP Test step and getModelFits() for the modeling step
Arguments
- posterior_list
An object of class 'postList' as created by getPosterior() containing information about the (mixture) posterior distribution per dose group
- contr
An object of class 'optContr' as created by the getContr() function. It contains the contrast matrix to be used for the testing step.
- crit_prob_adj
A getCritProb object, specifying the critical value to be used for the testing (on the probability scale).
- simple
Boolean variable, defining whether simplified fit will be applied. Passed to the getModelFits() function. Default FALSE.
Examples
mods <- DoseFinding::Mods(linear = NULL,
linlog = NULL,
emax = c(0.5, 1.2),
exponential = 2,
doses = c(0, 0.5, 2,4, 8))
dose_levels <- c(0, 0.5, 2, 4, 8)
sd_posterior <- c(2.8, 3, 2.5, 3.5, 4)
contr_mat <- getContr(
mods = mods,
dose_levels = dose_levels,
sd_posterior = sd_posterior)
critVal <- getCritProb(
mods = mods,
dose_weights = c(50, 50, 50, 50, 50), #reflecting the planned sample size
dose_levels = dose_levels,
alpha_crit_val = 0.05)
prior_list <- list(Ctrl = RBesT::mixnorm(comp1 = c(w = 1, m = 0, s = 5), sigma = 2),
DG_1 = RBesT::mixnorm(comp1 = c(w = 1, m = 1, s = 12), sigma = 2),
DG_2 = RBesT::mixnorm(comp1 = c(w = 1, m = 1.2, s = 11), sigma = 2) ,
DG_3 = RBesT::mixnorm(comp1 = c(w = 1, m = 1.3, s = 11), sigma = 2) ,
DG_4 = RBesT::mixnorm(comp1 = c(w = 1, m = 2, s = 13), sigma = 2))
mu <- c(0, 1, 1.5, 2, 2.5)
S_hat <- c(5, 4, 6, 7, 8)
posterior_list <- getPosterior(
prior_list = prior_list,
mu_hat = mu,
S_hat = S_hat)
performBayesianMCPMod(posterior_list = posterior_list,
contr = contr_mat,
crit_prob_adj = critVal,
simple = FALSE)
#> Bayesian Multiple Comparison Procedure
#> Summary:
#> Sign: 0
#> Critical Probability: 0.9752816
#> Maximum Posterior Probability: 0.6446552
#>
#> Posterior Probabilities for Model Shapes:
#> Model Probability
#> linear 0.6191201
#> linlog 0.6446552
#> emax1 0.6412959
#> emax2 0.6410572
#> exponential 0.5839261