Marginal Posterior Predictions from a 'brms' Model

Calculate marginal predictions from a brms model. Marginal predictions average over the input data for each posterior draw. Marginal predictions for models with random effects will integrate over random effects. Arguments are labeled as required when it is required that the user directly specify the argument. Arguments are labeled as optional when either the argument is optional or there are sensible default values so that users do not typically need to specify the argument.

Usage

prediction(
  object,
  data,
  summarize = TRUE,
  posterior = FALSE,
  index,
  dpar = NULL,
  resample = 0L,
  resampleseed = FALSE,
  effects = c("fixedonly", "includeRE", "integrateoutRE"),
  backtrans = c("response", "linear", "identity", "invlogit", "exp", "square",
    "inverse"),
  k = 100L,
  raw = FALSE,
  ...
)

Arguments

object: A required argument specifying a fitted brms model object.
data: A required argument specifying a data frame or data table passed to fitted() as the new data to be used for predictions.
summarize: An optional argument, a logical value, whether or not to calculate summaries of the posterior predictions. Defaults to TRUE.
posterior: An optional argument, a logical value whether or not to save and return the posterior samples. Defaults to FALSE as the assumption is a typical use case is to return the summaries only.
index: An optional argument, an integer vector, giving the posterior draws to be used in the calculations. If omitted, defaults to all posterior draws.
dpar: An optional argument, the parameter passed on to the dpar argument of fitted() in brms. Defaults to NULL indicating the mean or location parameter typically.
resample: An optional argument, an integer indicating the number of bootstrap resamples of the posterior predictions to use when calculating summaries. Defaults to 0L. See documentation from .averagePosterior() for more details. This should be considered experimental.
resampleseed: An optional argument, a seed for random number generation. Defaults to FALSE, which means no seed is set. Only used if resample is a positive, non-zero integer. See documentation from .averagePosterior() for more details. This should be considered experimental.
effects: An optional argument, a character string indicating the type of prediction to be made. Can be one of “fixedonly” meaning only use fixed effects, “includeRE” meaning that random effects should be included in the predictions, or “integrateoutRE” meaning that random effects should be integrated out / over in the predictions. It defaults to “fixedonly” so is not typically required for a user to specify it.
backtrans: An optional argument, a character string indicating the type of back transformation to be applied. Can be one of “response” meaning to use the response scale, “linear” or “identity” meaning to use the linear predictor scale, or a specific back transformation desired, from a possible list of “invlogit”, “exp”, “square”, or “inverse”. Custom back transformations should only be needed if, for example, the outcome variable was transformed prior to fitting the model. It defaults to “response” so is not typically required for a user to specify it.
k: An optional argument, an integer providing the number of random draws to use for integrating out the random effects. Only relevant when effects is “integrateoutRE”. It defaults to 100L, a rather arbitrary number attempting to balance the increased precision that comes from a larger value, with the increased computational cost of more Monte Carlo simulations when integrating out random effects.
raw: An optional argument, a logical value indicating whether to return the raw output or to average over the Monte Carlo samples. Defaults to FALSE. Setting it to TRUE can be useful if you want not only the full posterior distribution but also the k Monte Carlo samples used for the numerical integration. This cannot be used with summarize = TRUE.
...: An optional argument, additional arguments passed to fitted().

Value

A list with Summary and Posterior. Some of these may be NULL depending on the arguments used.

References

Pavlou, M., Ambler, G., Seaman, S., & Omar, R. Z. (2015) doi:10.1186/s12874-015-0046-6 “A note on obtaining correct marginal predictions from a random intercepts model for binary outcomes” and Skrondal, A., & Rabe-Hesketh, S. (2009) doi:10.1111/j.1467-985X.2009.00587.x “Prediction in multilevel generalized linear models”