pp_average.brmsfit {brms} | R Documentation |

## Posterior predictive draws averaged across models

### Description

Compute posterior predictive draws averaged across models. Weighting can be done in various ways, for instance using Akaike weights based on information criteria or marginal likelihoods.

### Usage

```
## S3 method for class 'brmsfit'
pp_average(
x,
...,
weights = "stacking",
method = "posterior_predict",
ndraws = NULL,
nsamples = NULL,
summary = TRUE,
probs = c(0.025, 0.975),
robust = FALSE,
model_names = NULL,
control = list(),
seed = NULL
)
pp_average(x, ...)
```

### Arguments

`x` |
A |

`...` |
More |

`weights` |
Name of the criterion to compute weights from. Should be one
of |

`method` |
Method used to obtain predictions to average over. Should be
one of |

`ndraws` |
Total number of posterior draws to use. |

`nsamples` |
Deprecated alias of |

`summary` |
Should summary statistics
(i.e. means, sds, and 95% intervals) be returned
instead of the raw values? Default is |

`probs` |
The percentiles to be computed by the |

`robust` |
If |

`model_names` |
If |

`control` |
Optional |

`seed` |
A single numeric value passed to |

### Details

Weights are computed with the `model_weights`

method.

### Value

Same as the output of the method specified
in argument `method`

.

### See Also

`model_weights`

, `posterior_average`

### Examples

```
## Not run:
# model with 'treat' as predictor
fit1 <- brm(rating ~ treat + period + carry, data = inhaler)
summary(fit1)
# model without 'treat' as predictor
fit2 <- brm(rating ~ period + carry, data = inhaler)
summary(fit2)
# compute model-averaged predicted values
(df <- unique(inhaler[, c("treat", "period", "carry")]))
pp_average(fit1, fit2, newdata = df)
# compute model-averaged fitted values
pp_average(fit1, fit2, method = "fitted", newdata = df)
## End(Not run)
```

*brms*version 2.21.0 Index]