Compute posterior predictions of smooth `s`

and `t2`

terms of
models fitted with brms.

```
# S3 method for 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 `brmsfit`

.

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

- newdata
An optional `data.frame`

for which to evaluate
predictions. If `NULL`

(default), the original data of the model is
used. Only those variables appearing in the chosen `smooth`

term are
required.

- 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 `NULL`

(the default) all draws are used. Ignored if
`draw_ids`

is not `NULL`

.

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

(the default), all draws are used.

- ...
Currently ignored.

## Value

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

## Examples

```
if (FALSE) {
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))
}
```