brms (version 2.9.0)

fitted.brmsfit: Extract Model Fitted Values of brmsfit Objects

Description

Predict expected values of the response distribution (i.e., predict the 'regression line') for a 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 smaller variance than the response predictions performed by the predict method. This is because the residual error is not incorporated. The estimated means of both methods should, however, be very similar.

Usage

# S3 method for brmsfit
fitted(object, newdata = NULL, re_formula = NULL,
  scale = c("response", "linear"), resp = NULL, dpar = NULL,
  nlpar = NULL, nsamples = NULL, subset = NULL, sort = FALSE,
  summary = TRUE, robust = FALSE, probs = c(0.025, 0.975), ...)
# S3 method for brmsfit
posterior_linpred(object, transform = FALSE,
  newdata = NULL, re_formula = NULL, re.form = NULL, resp = NULL,
  dpar = NULL, nlpar = NULL, nsamples = NULL, subset = 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.

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.

scale

Either "response" or "linear". If "response" results are returned on the scale of the response variable. If "linear" fitted values are returned on the scale of the linear predictor.

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, fitted values of this parameters are returned.

nlpar

Optional name of a predicted non-linear parameter. If specified, fitted values of this parameters are returned.

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).

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.

transform

Logical; alias of scale. If TRUE, scale is set to "response". If FALSE, scale is set to "linear". Only implemented for compatibility with the posterior_linpred generic.

re.form

Alias of re_formula.

Value

Fitted values extracted from object. The output depends on the family: If summary = TRUE it is a N x E x C array for categorical and ordinal models and a N x E matrix else. If summary = FALSE it is a S x N x C array for categorical and ordinal models and a S x N matrix else. N is the number of observations, S is the number of samples, C is the number of categories, and E is equal to length(probs) + 2. 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_linpred.brmsfit is an alias of fitted.brmsfit with scale = "linear" and summary = FALSE.

Examples

# NOT RUN {
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject), 
           data = inhaler)

## extract fitted values
fitted_values <- fitted(fit)
head(fitted_values)

## plot fitted means against actual response
dat <- as.data.frame(cbind(Y = standata(fit)$Y, fitted_values))
ggplot(dat) + geom_point(aes(x = Estimate, y = Y))
# }
# NOT RUN {
# }