posterior_smooths.brmsfit: Posterior Predictions of Smooth Terms

In brms: Bayesian Regression Models using 'Stan'

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)

brms documentation built on Sept. 26, 2023, 1:08 a.m.