brms (version 2.1.0)

marginal_smooths.brmsfit: Display Smooth Terms

Description

Display smooth s and t2 terms of models fitted with brms.

Usage

# S3 method for brmsfit
marginal_smooths(x, smooths = NULL, probs = c(0.025,
  0.975), spaghetti = FALSE, resolution = 100, too_far = 0,
  subset = NULL, nsamples = NULL, ...)
marginal_smooths(x, ...)

Arguments

x

An R object usually of class brmsfit.

smooths

Optional character vector of smooth terms to display. If NULL (the default) all smooth terms are shown.

probs

The quantiles to be used in the computation of credible intervals (defaults to 2.5 and 97.5 percent quantiles)

spaghetti

Logical. Indicates if predictions should be visualized via spaghetti plots. Only applied for numeric predictors. If TRUE, it is recommended to set argument nsamples to a relatively small value (e.g. 100) in order to reduce computation time.

resolution

Number of support points used to generate the plots. Higher resolution leads to smoother plots. Defaults to 100. If surface is TRUE, this implies 10000 support points for interaction terms, so it might be necessary to reduce resolution when only few RAM is available.

too_far

Positive number. For surface plots only: Grid points that are too far away from the actual data points can be excluded from the plot. too_far determines what is too far. The grid is scaled into the unit square and then grid points more than too_far from the predictor variables are excluded. By default, all grid points are used. Ignored for non-surface plots.

subset

A numeric vector specifying the posterior samples to be used. If NULL (the default), all samples are used.

nsamples

Positive integer indicating how many posterior samples should be used. If NULL (the default) all samples are used. Ignored if subset is not NULL.

...

Currently ignored.

Value

For the brmsfit method, an object of class brmsMarginalEffects. See marginal_effects for more details and documentation of the related plotting function.

Details

Two-dimensional smooth terms will be visualized using either contour or raster plots.

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)
# show all smooth terms
plot(marginal_smooths(fit), rug = TRUE, ask = FALSE)
# show only the smooth term s(x2)
plot(marginal_smooths(fit, smooths = "s(x2)"), ask = FALSE)

# fit and plot a two-dimensional smooth term
fit2 <- brm(y ~ t2(x0, x2), data = dat)
ms <- marginal_smooths(fit2)
plot(ms, stype = "contour")
plot(ms, stype = "raster")
# }
# NOT RUN {
# }