R: Posterior Predictions of Smooth Terms

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 `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

    ## 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)

[Package brms version 2.16.3 Index]