R: Expected Values of the Posterior Predictive Distribution

fitted.brmsfit {brms} | R Documentation

## Expected Values of the Posterior Predictive Distribution

### Description

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

### Usage

    ## S3 method for class 'brmsfit'
    fitted(
      object,
      newdata = NULL,
      re_formula = NULL,
      scale = c("response", "linear"),
      resp = NULL,
      dpar = NULL,
      nlpar = NULL,
      ndraws = NULL,
      draw_ids = NULL,
      sort = FALSE,
      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 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.
`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"`, results are returned on the scale of the linear predictor term, that is without applying the inverse link function or other transformations.
`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, expected predictions of this parameters are returned.
`nlpar` | Optional name of a predicted non-linear parameter. If specified, expected predictions of this parameters are returned.
`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`).
`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 mean response values. If `summary = FALSE` the output resembles those of `posterior_epred.brmsfit`.

If `summary = TRUE` the output depends on the family: For categorical and ordinal families, the output is an N x E x C array, where N is the number of observations, E is the number of summary statistics, and C is the number of categories. For all other families, the output is an N x E matrix. The number of summary statistics E is equal to `2 + length(probs)`: 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`.

In multivariate models, an additional dimension is added to the output which indexes along the different response variables.

### See Also

`posterior_epred.brmsfit`

### Examples

    ## Not run:
    ## fit a model
    fit <- brm(rating ~ treat + period + carry + (1|subject),
               data = inhaler)

    ## compute expected predictions
    fitted_values <- fitted(fit)
    head(fitted_values)

    ## plot expected predictions against actual response
    dat <- as.data.frame(cbind(Y = standata(fit)$Y, fitted_values))
    ggplot(dat) + geom_point(aes(x = Estimate, y = Y))

    ## End(Not run)

[Package brms version 2.17.0 Index]