R: Model Predictions of 'brmsfit' Objects

predict.brmsfit {brms} | R Documentation

## 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 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 class '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 class '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`.

### 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`.

### 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.

### 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)

    ## End(Not run)

[Package brms version 2.6.0 Index]