Summaries of the posterior predictive quantities — frmtmb-draws-summaries • frmtmb.sample

brms's three summarizing methods, each a summary of the draws method beside it:

fitted() summarizes posterior_epred() (or posterior_linpred() under scale = "linear");
predict() summarizes posterior_predict();
residuals() summarizes predictive_error().

summary = FALSE returns the draws themselves, which is what the method it wraps returns.

Usage

# S3 method for class 'frmtmb_draws'
fitted(
  object,
  newdata = NULL,
  re_formula = arg_unset(),
  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),
  ...
)

# S3 method for class 'frmtmb_draws'
predict(
  object,
  newdata = NULL,
  re_formula = arg_unset(),
  transform = NULL,
  resp = NULL,
  negative_rt = FALSE,
  ndraws = NULL,
  draw_ids = NULL,
  sort = FALSE,
  ntrys = NULL,
  cores = NULL,
  summary = TRUE,
  robust = FALSE,
  probs = c(0.025, 0.975),
  ...
)

# S3 method for class 'frmtmb_draws'
residuals(
  object,
  newdata = NULL,
  re_formula = arg_unset(),
  method = "posterior_predict",
  type = c("ordinary", "pearson"),
  resp = NULL,
  ndraws = NULL,
  draw_ids = NULL,
  sort = FALSE,
  summary = TRUE,
  robust = FALSE,
  probs = c(0.025, 0.975),
  ...
)

Arguments

object: A frmtmb_draws from frm_sample().
newdata, re_formula, resp, dpar, nlpar, ndraws, draw_ids: Passed to the draws method, where they are documented.
scale: "response" for the expected response, "linear" for the linear predictor.
summary: If FALSE, the draws instead of their summary.
robust: If TRUE, the median and MAD instead of the mean and standard deviation.
probs: Probabilities of the quantile columns.
...: Passed to the draws method, which is where brms's point_estimate and ndraws_point_estimate are answered.
transform: Applied to the draws before they are summarized.
negative_rt, ntrys, cores, sort: brms's remaining arguments, carried so that a positional brms call asks the same question; each is passed on or refused by the method it belongs to.
method: For residuals(), which predictive distribution the error is taken against.
type: For residuals(), "ordinary" (brms's name for the raw error) or "pearson".

Value

With summary = TRUE an observations-by-four matrix, or an observations-by-four-by-K array for a category distribution. With summary = FALSE the draws.