trialr (version 0.1.6)

prior_predictive_augbin_2t_1a: Sample data from the Augmented Binary model prior predictive distribution.

Description

Sample data from the prior predictive distributions of the two-period, single arm Augmented Binary model, subject to chosen prior parameters.

Usage

prior_predictive_augbin_2t_1a(
  num_samps,
  alpha_mean,
  alpha_sd,
  beta_mean,
  beta_sd,
  gamma_mean,
  gamma_sd,
  sigma_mean,
  sigma_sd,
  omega_lkj_eta,
  alpha_d1_mean,
  alpha_d1_sd,
  gamma_d1_mean,
  gamma_d1_sd,
  alpha_d2_mean,
  alpha_d2_sd,
  gamma_d2_mean,
  gamma_d2_sd
)

Value

Object of class tibble

Arguments

num_samps

Number of samples.

alpha_mean

Prior mean of alpha parameter.

alpha_sd

Prior sd of alpha parameter.

beta_mean

Prior mean of beta parameter.

beta_sd

Prior sd of beta parameter.

gamma_mean

Prior mean of gamma parameter.

gamma_sd

Prior sd of gamma parameter.

sigma_mean

Prior mean of sigma parameter.

sigma_sd

Prior sd of sigma parameter.

omega_lkj_eta

Prior eta parameter for LKJ prior on covariance matrix of log tumour sizes.

alpha_d1_mean

Prior mean of alpha_D1 parameter.

alpha_d1_sd

Prior sd of alpha_D1 parameter.

gamma_d1_mean

Prior mean of gamma_D1 parameter.

gamma_d1_sd

Prior sd of gamma_D1 parameter.

alpha_d2_mean

Prior mean of alpha_D2 parameter.

alpha_d2_sd

Prior sd of alpha_D2 parameter.

gamma_d2_mean

Prior mean of gamma_D2 parameter.

gamma_d2_sd

Prior sd of gamma_D2 parameter.

See Also

stan_augbin

Examples

prior_predictive_augbin_2t_1a(num_samps = 1000,
                              alpha_mean = 0, alpha_sd = 1,
                              beta_mean = 0, beta_sd = 1,
                              gamma_mean = 0, gamma_sd = 1,
                              sigma_mean = 0, sigma_sd = 1,
                              omega_lkj_eta = 1,
                              alpha_d1_mean = 0, alpha_d1_sd = 1,
                              gamma_d1_mean = 0, gamma_d1_sd = 1,
                              alpha_d2_mean = 0, alpha_d2_sd = 1,
                              gamma_d2_mean = 0, gamma_d2_sd = 1)