Posterior Draws of the Linear Predictor

Compute posterior draws of the linear predictor, that is draws before applying any link functions or other transformations. Can be performed for the data used to fit the model (posterior predictive checks) or for new data.

Usage

# S3 method for class 'brmsfit'
posterior_linpred(
  object,
  transform = FALSE,
  newdata = NULL,
  re_formula = NULL,
  re.form = NULL,
  resp = NULL,
  dpar = NULL,
  nlpar = NULL,
  incl_thres = NULL,
  ndraws = NULL,
  draw_ids = NULL,
  sort = FALSE,
  ...
)

Arguments

object: An object of class brmsfit.
transform: Logical; if FALSE (the default), draws of the linear predictor are returned. If TRUE, draws of the transformed linear predictor, that is, after applying the inverse link function are returned.
newdata: An optional data.frame for which to evaluate predictions. If NULL (default), the original data of the model is used. NA values within factors (excluding grouping variables) are interpreted as if all dummy variables of this factor are zero. This allows, for instance, to make predictions of the grand mean when using sum coding. NA values within grouping variables are treated as a new level.
re_formula: formula containing group-level effects to be considered in the prediction. If NULL (default), include all group-level effects; if NA or ~0, include no group-level effects.
re.form: Alias of re_formula.
resp: Optional names of response variables. If specified, predictions are performed only for the specified response variables.
dpar: Name of a predicted distributional parameter for which draws are to be returned. By default, draws of the main distributional parameter(s) "mu" are returned.
nlpar: Optional name of a predicted non-linear parameter. If specified, expected predictions of this parameters are returned.
incl_thres: Logical; only relevant for ordinal models when transform is FALSE, and ignored otherwise. Shall the thresholds and category-specific effects be included in the linear predictor? For backwards compatibility, the default is to not include them.
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.
sort: Logical. Only relevant for time series models. Indicating whether to return predicted values in the original order (FALSE; default) or in the order of the time series (TRUE).
...: Further arguments passed to prepare_predictions that control several aspects of data validation and prediction.

Examples

# \dontrun{
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject),
           data = inhaler)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 5.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.54 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1:                2.662 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 4.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:                2.404 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 4.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3:                2.346 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 4.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
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#> Chain 4: 

## extract linear predictor values
pl <- posterior_linpred(fit)
str(pl)
#>  num [1:4000, 1:572] 1.377 0.956 1.008 1.488 1.371 ...
# }

Usage

Arguments

See also

Examples