brms (version 2.16.1)

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

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.

re_formula

Optional formula containing group-level effects to be considered in the prediction. If NULL, include all group-level effects; if NA (default), include no group-level effects.

epred

Logical. If TRUE compute predictions of the posterior predictive distribution's mean (see posterior_epred.brmsfit) while ignoring arguments dpar and nlpar. Defaults to FALSE.

...

Additional arguments passed to emmeans.

Details

In order to ensure compatibility of most brms models with emmeans, predictions are not generated 'manually' via a design matrix and coefficient vector, but rather via posterior_linpred.brmsfit. This appears to generally work well, but note that it produces an `.@linfct` slot that contains the computed predictions as columns instead of the coefficients.

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)

# obtain estimates for the posterior predictive distribution's mean
epred <- emmeans(fit, "disease", epred = TRUE)
summary(epred, point.est = mean)
# }