R: Extract Model Fitted Values of 'brmsfit' Objects

fitted.brmsfit {brms} | R Documentation

## Extract Model Fitted Values of `brmsfit` Objects

### Description

Predict mean values of the response distribution (i.e., 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 measurement error is not incorporated. The estimated means of both methods should, however, be very similar.

### Usage

    ## S3 method for class '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 class '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`.

### 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`.

### 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.

### 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))

    ## End(Not run)

[Package brms version 2.6.0 Index]