| marginal_smooths.brmsfit {brms} | R Documentation |
Display smooth s and t2 terms of models
fitted with brms.
## S3 method for class 'brmsfit' marginal_smooths(x, smooths = NULL, int_conditions = NULL, probs = c(0.025, 0.975), spaghetti = FALSE, resolution = 100, too_far = 0, subset = NULL, nsamples = NULL, ...) marginal_smooths(x, ...)
x |
An R object usually of class |
smooths |
Optional character vector of smooth terms
to display. If |
int_conditions |
An optional named |
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 |
resolution |
Number of support points used to generate
the plots. Higher resolution leads to smoother plots.
Defaults to |
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.
|
subset |
A numeric vector specifying
the posterior samples to be used.
If |
nsamples |
Positive integer indicating how many
posterior samples should be used.
If |
... |
Currently ignored. |
Two-dimensional smooth terms will be visualized using either contour or raster plots.
For the brmsfit method,
an object of class brmsMarginalEffects. See
marginal_effects for
more details and documentation of the related plotting function.
## 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") ## End(Not run)