mem_mcmc: Fit the MEM Model using MCMC
In basket: Basket Trial Analysis

Fit the MEM model using Bayesian Metropolis-Hasting MCMC inference.

mem_mcmc(
  responses,
  size,
  name,
  p0 = 0.15,
  shape1 = 0.5,
  shape2 = 0.5,
  prior = diag(length(responses))/2 + matrix(0.5, nrow = length(responses), ncol =
    length(responses)),
  hpd_alpha = 0.05,
  alternative = "greater",
  mcmc_iter = 2e+05,
  mcmc_burnin = 50000,
  initial_mem = round(prior - 0.001),
  seed = 1000,
  cluster_analysis = FALSE,
  call = NULL,
  cluster_function = cluster_membership
)

`responses`	the number of responses in each basket.
`size`	the size of each basket.
`name`	the name of each basket.
`p0`	the null response rate for the poster probability calculation (default 0.15).
`shape1`	the first shape parameter(s) for the prior of each basket (default 0.5).
`shape2`	the second shape parameter(s) for the prior of each basket (default 0.5).
`prior`	the matrix giving the prior inclusion probability for each pair of baskets. The default is on on the main diagonal and 0.5 elsewhere.
`hpd_alpha`	the highest posterior density trial significance.
`alternative`	the alternative case definition (default greater)
`mcmc_iter`	the number of MCMC iterations.
`mcmc_burnin`	the number of MCMC Burn_in iterations.
`initial_mem`	the initial MEM matrix.
`seed`	the random number seed.
`cluster_analysis`	if the cluster analysis is conducted.
`call`	the call of the function.
`cluster_function`	a function to cluster baskets

# 3 baskets, each with enrollement size 5
trial_sizes <- rep(5, 3)

# The response rates for the baskets.
resp_rate <- 0.15

# The trials: a column of the number of responses and a column of the
# the size of each trial.
trials <- data.frame(
  responses = rbinom(trial_sizes, trial_sizes, resp_rate),
  size = trial_sizes,
  name = letters[1:3]
)
res <- mem_mcmc(trials$responses, trials$size)