mem_mcmc {basket}

Fit the MEM Model using MCMC

Description

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

Usage

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
)

Arguments

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

Examples

# 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)