posterior_smooths.brmsfit {brms} | R Documentation |

## Posterior Predictions of Smooth Terms

### Description

Compute posterior predictions of smooth `s`

and `t2`

terms of
models fitted with brms.

### Usage

```
## S3 method for class 'brmsfit'
posterior_smooths(
object,
smooth,
newdata = NULL,
resp = NULL,
dpar = NULL,
nlpar = NULL,
ndraws = NULL,
draw_ids = NULL,
...
)
posterior_smooths(object, ...)
```

### Arguments

`object` |
An object of class |

`smooth` |
Name of a single smooth term for which predictions should be computed. |

`newdata` |
An optional |

`resp` |
Optional names of response variables. If specified, predictions are performed only for the specified response variables. |

`dpar` |
Optional name of a predicted distributional parameter. If specified, expected predictions of this parameters are returned. |

`nlpar` |
Optional name of a predicted non-linear parameter. If specified, expected predictions of this parameters are returned. |

`ndraws` |
Positive integer indicating how many posterior draws should
be used. If |

`draw_ids` |
An integer vector specifying the posterior draws to be used.
If |

`...` |
Currently ignored. |

### Value

An S x N matrix, where S is the number of posterior draws and N is the number of observations.

### Examples

```
## Not run:
set.seed(0)
dat <- mgcv::gamSim(1, n = 200, scale = 2)
fit <- brm(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat)
summary(fit)
newdata <- data.frame(x2 = seq(0, 1, 10))
str(posterior_smooths(fit, smooth = "s(x2)", newdata = newdata))
## End(Not run)
```

*brms*version 2.21.0 Index]