Draws from the Posterior Predictive Distribution

This method is an alias of posterior_predict.brmsfit with additional arguments for obtaining summaries of the computed draws.

Usage

# S3 method for class 'brmsfit'
predict(
  object,
  newdata = NULL,
  re_formula = NULL,
  transform = NULL,
  resp = NULL,
  negative_rt = FALSE,
  ndraws = NULL,
  draw_ids = NULL,
  sort = FALSE,
  ntrys = 5,
  cores = NULL,
  summary = TRUE,
  robust = FALSE,
  probs = c(0.025, 0.975),
  ...
)

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. 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.
transform: (Deprecated) A function or a character string naming a function to be applied on the predicted responses before summary statistics are computed.
resp: Optional names of response variables. If specified, predictions are performed only for the specified response variables.
negative_rt: Only relevant for Wiener diffusion models. A flag indicating whether response times of responses on the lower boundary should be returned as negative values. This allows to distinguish responses on the upper and lower boundary. Defaults to FALSE.
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).
ntrys: Parameter used in rejection sampling for truncated discrete models only (defaults to 5). See Details for more information.
cores: Number of cores (defaults to 1). On non-Windows systems, this argument can be set globally via the mc.cores option.
summary: Should summary statistics be returned instead of the raw values? Default is TRUE.
robust: If FALSE (the default) the mean is used as the measure of central tendency and the standard deviation as the measure of variability. If TRUE, the median and the median absolute deviation (MAD) are applied instead. Only used if summary is TRUE.
probs: The percentiles to be computed by the quantile function. Only used if summary is TRUE.
...: Further arguments passed to prepare_predictions that control several aspects of data validation and prediction.

Value

An array of predicted response values. If summary = FALSE the output resembles those of posterior_predict.brmsfit.

If summary = TRUE the output depends on the family: For categorical and ordinal families, the output is an N x C matrix, where N is the number of observations, C is the number of categories, and the values are predicted category probabilities. For all other families, the output is a N x E matrix where E = 2 + length(probs) is the number of summary statistics: The Estimate column contains point estimates (either mean or median depending on argument robust), while the Est.Error column contains uncertainty estimates (either standard deviation or median absolute deviation depending on argument robust). The remaining columns starting with Q contain quantile estimates as specified via argument probs.

Examples

# \dontrun{
## fit a model
fit <- brm(time | cens(censored) ~ age + sex + (1 + age || patient),
           data = kidney, family = "exponential", init = "0")
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.29 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                2.054 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2.3e-05 seconds
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 2.3e-05 seconds
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#> Chain 4: 

## predicted responses
pp <- predict(fit)
head(pp)
#>       Estimate Est.Error      Q2.5     Q97.5
#> [1,]  35.34280  47.65143 0.6382854  170.6624
#> [2,] 160.42088 296.89426 2.4560518  802.2983
#> [3,]  41.34877  55.35052 0.7301142  186.6020
#> [4,] 296.71247 384.85454 5.5359669 1221.5351
#> [5,]  46.87249  68.61577 0.8644541  225.8016
#> [6,] 198.86530 255.89102 3.7154450  910.0475

## predicted responses excluding the group-level effect of age
pp <- predict(fit, re_formula = ~ (1 | patient))
head(pp)
#>       Estimate Est.Error      Q2.5     Q97.5
#> [1,]  39.90130  52.84751 0.6616917  182.3723
#> [2,] 170.28120 595.24640 3.3010333  722.7105
#> [3,]  43.76757  56.27128 0.9051928  192.3669
#> [4,] 259.75823 324.36563 4.6723401 1087.5806
#> [5,]  48.62304  79.00334 0.9731580  242.6778
#> [6,] 197.38136 253.26588 3.4668561  878.3151

## predicted responses of patient 1 for new data
newdata <- data.frame(
  sex = factor(c("male", "female")),
  age = c(20, 50),
  patient = c(1, 1)
)
predict(fit, newdata = newdata)
#>       Estimate Est.Error      Q2.5    Q97.5
#> [1,]  37.68225  51.47541 0.7504945 164.7520
#> [2,] 142.02674 248.87428 1.9691851 711.8125
# }

Draws from the Posterior Predictive Distribution

Usage

Arguments

Value

See also

Examples