mem_mcmc {basket} | R Documentation |

## 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)
```

*basket*version 0.10.11 Index]