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#' Simple helper function to demonstrate fitting of an Augmented Binary model.
#'
#' This function exist mostly to demonstrate things you can do to instances of
#' \code{\link{augbin_fit}} without having to paste into each example the not
#' inconsiderable blob of code to sample outcomes and fit the model.
#'
#' @return instance of \code{\link{augbin_fit}}
#'
#' @seealso
#' \code{\link{stan_augbin}}
#' \code{\link{augbin_fit}}
#' \code{\link{prior_predictive_augbin_2t_1a}}
#' \code{\link[rstan:sampling]{sampling}}
#'
#' @importFrom stats runif rbinom
#' @export
#'
#' @examples
#' \dontrun{
#' fit <- stan_augbin_demo()
#' # I told you it was simple.
#' }
stan_augbin_demo <- function() {
priors <- list(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)
# Scenario 1 of Table 1 in Wason & Seaman (2013)
N <- 50
sigma <- 1
delta1 <- -0.356
mu <- c(0.5 * delta1, delta1)
Sigma = matrix(c(0.5 * sigma^2, 0.5 * sigma^2, 0.5 * sigma^2, sigma^2),
ncol = 2)
alphaD <- -1.5
gammaD <- 0
set.seed(123456)
y <- MASS::mvrnorm(n = N, mu, Sigma)
z0 <- runif(N, min = 5, max = 10)
z1 <- exp(y[, 1]) * z0
z2 <- exp(y[, 2]) * z0
d1 <- rbinom(N, size = 1, prob = gtools::inv.logit(alphaD + gammaD * z0))
d2 <- rbinom(N, size = 1, prob = gtools::inv.logit(alphaD + gammaD * z1))
tumour_size <- data.frame(z0, z1, z2) # Sizes in cm
non_shrinkage_failure <- data.frame(d1, d2)
# Fit
## Not run:
fit <- stan_augbin(tumour_size, non_shrinkage_failure,
prior_params = priors, model = '2t-1a', seed = 123)
fit
}
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