predict.brmsfit

0th

Percentile

Model Predictions of brmsfit Objects

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 fitted values (i.e., the 'regression line') performed by the fitted method. This is because the measurement 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, allow_new_levels = FALSE,
  sample_new_levels = "uncertainty", new_objects = list(),
  incl_autocor = TRUE, negative_rt = FALSE, subset = NULL,
  nsamples = NULL, sort = FALSE, nug = NULL, ntrys = 5,
  summary = TRUE, robust = FALSE, probs = c(0.025, 0.975), ...)

# S3 method for brmsfit posterior_predict(object, newdata = NULL, re_formula = NULL, transform = NULL, allow_new_levels = FALSE, sample_new_levels = "uncertainty", new_objects = list(), incl_autocor = TRUE, negative_rt = FALSE, subset = NULL, nsamples = NULL, sort = FALSE, nug = NULL, ntrys = 5, 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 orginal 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.

allow_new_levels

A flag indicating if new levels of group-level effects are allowed (defaults to FALSE). Only relevant if newdata is provided.

sample_new_levels

Indicates how to sample new levels for grouping factors specified in re_formula. This argument is only relevant if newdata is provided and allow_new_levels is set to TRUE. If "uncertainty" (default), include group-level uncertainty in the predictions based on the variation of the existing levels. If "gaussian", sample new levels from the (multivariate) normal distribution implied by the group-level standard deviations and correlations. This options may be useful for conducting Bayesian power analysis. If "old_levels", directly sample new levels from the existing levels.

new_objects

A named list of objects containing new data, which cannot be passed via argument newdata. Currently, only required for objects passed to cor_sar and cor_fixed.

incl_autocor

A flag indicating if ARMA autocorrelation parameters should be included in the predictions. Defaults to TRUE. Setting it to FALSE will not affect other correlation structures such as cor_bsts, or cor_fixed.

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 distinquish responses on the upper and lower boundary. Defaults to FALSE.

subset

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

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.

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).

nug

Small positive number for Gaussian process terms only. For numerical reasons, the covariance matrix of a Gaussian process might not be positive definite. Adding a very small number to the matrix's diagonal often solves this problem. If NULL (the default), nug is chosen internally.

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 deivation (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.

...

Currently ignored.

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 occure suggesting to increase argument ntrys.

Value

Predicted values of the response variable. If summary = TRUE the output depends on the family: For catagorical 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.

Aliases
  • predict.brmsfit
  • posterior_predict.brmsfit
  • posterior_predict
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 {
# }
Documentation reproduced from package brms, version 1.10.2, License: GPL (>= 3)

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