Draws from the Expected Value of the Posterior Predictive Distribution
Source:R/posterior_epred.R
posterior_epred.brmsfit.RdCompute posterior draws of the expected value of the posterior predictive
distribution. Can be performed for the data used to fit the model (posterior
predictive checks) or for new data. By definition, these predictions have
smaller variance than the posterior predictions performed by the
posterior_predict.brmsfit method. This is because only the
uncertainty in the expected value of the posterior predictive distribution is
incorporated in the draws computed by posterior_epred while the
residual error is ignored there. However, the estimated means of both methods
averaged across draws should be very similar.
Usage
# S3 method for class 'brmsfit'
posterior_epred(
object,
newdata = NULL,
re_formula = NULL,
re.form = NULL,
resp = NULL,
dpar = NULL,
nlpar = NULL,
ndraws = NULL,
draw_ids = NULL,
sort = FALSE,
...
)Arguments
- object
An object of class
brmsfit.- newdata
An optional data.frame for which to evaluate predictions. If
NULL(default), the original data of the model is used.NAvalues 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.NAvalues 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; ifNAor~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
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.
- ndraws
Positive integer indicating how many posterior draws should be used. If
NULL(the default) all draws are used. Ignored ifdraw_idsis notNULL.- 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_predictionsthat control several aspects of data validation and prediction.
Value
An array of draws. For
categorical and ordinal models, the output is an S x N x C array.
Otherwise, the output is an S x N matrix, where S is the number of
posterior draws, N is the number of observations, and C is the number of
categories. In multivariate models, an additional dimension is added to the
output which indexes along the different response variables.
Details
NA values within factors in newdata,
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.
In multilevel models, it is possible to
allow new levels of grouping factors to be used in the predictions.
This can be controlled via argument allow_new_levels.
New levels can be sampled in multiple ways, which can be controlled
via argument sample_new_levels. Both of these arguments are
documented in prepare_predictions along with several
other useful arguments to control specific aspects of the predictions.
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 5.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.59 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
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#> Chain 1:
#> Chain 1: Elapsed Time: 1.608 seconds (Warm-up)
#> Chain 1: 0.772 seconds (Sampling)
#> Chain 1: 2.38 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 4.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
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#> Chain 2:
#> Chain 2: Elapsed Time: 1.59 seconds (Warm-up)
#> Chain 2: 0.78 seconds (Sampling)
#> Chain 2: 2.37 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:
#> Chain 3: Elapsed Time: 1.685 seconds (Warm-up)
#> Chain 3: 0.774 seconds (Sampling)
#> Chain 3: 2.459 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 4.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
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#> Chain 4:
#> Chain 4: Elapsed Time: 1.675 seconds (Warm-up)
#> Chain 4: 0.771 seconds (Sampling)
#> Chain 4: 2.446 seconds (Total)
#> Chain 4:
## compute expected predictions
ppe <- posterior_epred(fit)
str(ppe)
#> num [1:4000, 1:572] 1.53 0.907 1.474 1.016 1.277 ...
# }