brms (version 2.10.0)

predict.brmsfit: Model Predictions of brmsfit Objects

Description

Predict responses based on the fitted model. Can be performed for the data used to fit the model (posterior predictive checks) or for new data. By definition, these predictions have higher variance than predictions of the expected values of the response distribution (i.e., predictions of the 'regression line') performed by the fitted method. This is because the residual error is incorporated. The estimated means of both methods should, however, be very similar.

Usage

# S3 method for brmsfit
predict(object, newdata = NULL, re_formula = NULL,
  transform = NULL, resp = NULL, negative_rt = FALSE,
  nsamples = NULL, subset = NULL, sort = FALSE, ntrys = 5,
  summary = TRUE, robust = FALSE, probs = c(0.025, 0.975), ...)
# S3 method for brmsfit
posterior_predict(object, newdata = NULL,
  re_formula = NULL, re.form = NULL, transform = NULL, resp = NULL,
  negative_rt = FALSE, nsamples = NULL, subset = NULL,
  sort = FALSE, ntrys = 5, ...)

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.

re_formula

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

transform

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.

nsamples

Positive integer indicating how many posterior samples should be used. If NULL (the default) all samples are used. Ignored if subset is not NULL.

subset

A numeric vector specifying the posterior samples to be used. If NULL (the default), all samples 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.

summary

Should summary statistics (i.e. means, sds, and 95% intervals) 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 extract_draws that control several aspects of data validation and prediction.

re.form

Alias of re_formula.

Value

Predicted values of the response variable. If summary = TRUE the output depends on the family: For categorical and ordinal families, it is a N x C matrix, where N is the number of observations and C is the number of categories. For all other families, it is a N x E matrix where E is equal to length(probs) + 2. If summary = FALSE, the output is as a S x N matrix, where S is the number of samples. In multivariate models, the output is an array of 3 dimensions, where the third dimension indicates the 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.

Method posterior_predict.brmsfit is an alias of predict.brmsfit with summary = FALSE.

For truncated discrete models only: In the absence of any general algorithm to sample from truncated discrete distributions, rejection sampling is applied in this special case. This means that values are sampled until a value lies within the defined truncation boundaries. In practice, this procedure may be rather slow (especially in R). Thus, we try to do approximate rejection sampling by sampling each value ntrys times and then select a valid value. If all values are invalid, the closest boundary is used, instead. If there are more than a few of these pathological cases, a warning will occur suggesting to increase argument ntrys.

Examples

# NOT RUN {
## fit a model
fit <- brm(time | cens(censored) ~ age + sex + (1+age||patient), 
           data = kidney, family = "exponential", inits = "0")

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

## predicted responses excluding the group-level effect of age
pp2 <- predict(fit, re_formula = ~ (1|patient))
head(pp2)

## 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)
# }
# NOT RUN {
# }