brms (version 2.22.0)

predict.brmsfit: Draws from the Posterior Predictive Distribution

Description

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

Usage

# S3 method for 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),
  ...
)

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.

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.

See Also

posterior_predict.brmsfit

Examples

if (FALSE) {
## fit a model
fit <- brm(time | cens(censored) ~ age + sex + (1 + age || patient),
           data = kidney, family = "exponential", init = "0")

## predicted responses
pp <- predict(fit)
head(pp)

## predicted responses excluding the group-level effect of age
pp <- predict(fit, re_formula = ~ (1 | patient))
head(pp)

## 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)
}