bma {bmabasket} | R Documentation |

## Compute posterior model probabilities

### Description

Given data and hyperparameters, computes posterior model probabilities

### Usage

```
bma(pi0, y, n, P = NULL, mu0 = 0.5, phi0 = 1, priorModelProbs = NULL, pmp0 = 1)
```

### Arguments

`pi0` |
scalar or vector whose elements are between 0 and 1 giving threshold for the hypothesis test. If a scalar is provided, assumes same threshold for each basket |

`y` |
vector of responses |

`n` |
vector of sample sizes |

`P` |
integer giving maximum number of distinct parameters; default is all possible models |

`mu0` |
prior mean for beta prior |

`phi0` |
prior dispersion for beta prior |

`priorModelProbs` |
(optional) vector giving prior for models. Default is proportional to |

`pmp0` |
nonnegative scalar. Value of 0 corresponds to uniform prior across model space. Ignored if priorModelProbs is specified |

### Value

a list with the following structure:

- bmaProbs
model-averaged probabilities that each basket is larger than

`pi0`

- bmaMeans
model-averaged posterior mean for each basket

### Examples

```
## Simulate data with 3 baskets
probs <- c(0.5, 0.25, 0.25)
n <- rep(100, length(probs))
y <- rbinom(length(probs), size = n, prob = probs)
bma(0.5, y, n)
```

*bmabasket*version 0.1.2 Index]