brms: emmeans-brms-helpers – R documentation

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emmeans-brms-helpers

Support Functions for emmeans

Description

Functions required for compatibility of brms with emmeans. Users are not required to call these functions themselves. Instead, they will be called automatically by the emmeans function of the emmeans package.

Usage

recover_data.brmsfit(object, data, resp = NULL, dpar = NULL, nlpar = NULL, ...)

emm_basis.brmsfit(
  object,
  trms,
  xlev,
  grid,
  vcov.,
  resp = NULL,
  dpar = NULL,
  nlpar = NULL,
  ...
)

Arguments

`object`	An object of class `brmsfit`.
`data, trms, xlev, grid, vcov.`	Arguments required by emmeans.
`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.
`...`	Additional arguments passed to emmeans.

Details

In addition to the usual choices for dpar, the special value dpar = "mean" requests that we use the expected values of the posterior predictive distribution, obtained via posterior_epred.brmsfit.

Examples

## Not run: 
fit <- brm(time | cens(censored) ~ age * sex + disease + (1|patient),
            data = kidney, family = lognormal())
summary(fit)           

# summarize via 'emmeans'
library(emmeans)
rg <- ref_grid(fit)
em <- emmeans(rg, "disease")
summary(em, point.est = mean)

epred <- emmeans(fit, "disease", dpar = "mean")
summary(epred, point.est = mean)

## End(Not run)

brms

Bayesian Regression Models using 'Stan'

v2.15.0

GPL-2

Authors

Paul-Christian Bürkner [aut, cre], Jonah Gabry [ctb], Sebastian Weber [ctb], Andrew Johnson [ctb], Martin Modrak [ctb]

Initial release

2021-03-10