point_estimate: Point-estimates of posterior distributions

Description

Compute various point-estimates, such as the mean, the median or the MAP, to describe posterior distributions.

Usage

point_estimate(x, ...)
# S3 method for numeric
point_estimate(x, centrality = "all", dispersion = FALSE, threshold = 0.1, ...)
# S3 method for stanreg
point_estimate(
  x,
  centrality = "all",
  dispersion = FALSE,
  effects = c("fixed", "random", "all"),
  component = c("location", "all", "conditional", "smooth_terms", "sigma",
    "distributional", "auxiliary"),
  parameters = NULL,
  ...
)
# S3 method for brmsfit
point_estimate(
  x,
  centrality = "all",
  dispersion = FALSE,
  effects = c("fixed", "random", "all"),
  component = c("conditional", "zi", "zero_inflated", "all"),
  parameters = NULL,
  ...
)
# S3 method for BFBayesFactor
point_estimate(x, centrality = "all", dispersion = FALSE, ...)
# S3 method for get_predicted
point_estimate(
  x,
  centrality = "all",
  dispersion = FALSE,
  use_iterations = FALSE,
  verbose = TRUE,
  ...
)

Arguments

x: Vector representing a posterior distribution, or a data frame of such vectors. Can also be a Bayesian model. bayestestR supports a wide range of models (see, for example, methods("hdi")) and not all of those are documented in the 'Usage' section, because methods for other classes mostly resemble the arguments of the .numeric or .data.framemethods.
...: Additional arguments to be passed to or from methods.
centrality: The point-estimates (centrality indices) to compute. Character (vector) or list with one or more of these options: "median", "mean", "MAP" (see map_estimate()), "trimmed" (which is just mean(x, trim = threshold)), "mode" or "all".
dispersion: Logical, if TRUE, computes indices of dispersion related to the estimate(s) (SD and MAD for mean and median, respectively). Dispersion is not available for "MAP" or "mode" centrality indices.
threshold: For centrality = "trimmed" (i.e. trimmed mean), indicates the fraction (0 to 0.5) of observations to be trimmed from each end of the vector before the mean is computed.
effects: Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
component: Should results for all parameters, parameters for the conditional model or the zero-inflated part of the model be returned? May be abbreviated. Only applies to brms-models.
parameters: Regular expression pattern that describes the parameters that should be returned. Meta-parameters (like lp__ or prior_) are filtered by default, so only parameters that typically appear in the summary() are returned. Use parameters to select specific parameters for the output.
use_iterations: Logical, if TRUE and x is a get_predicted object, (returned by insight::get_predicted()), the function is applied to the iterations instead of the predictions. This only applies to models that return iterations for predicted values (e.g., brmsfit models).
verbose: Toggle off warnings.

References

Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., and Lüdecke, D. (2019). Indices of Effect Existence and Significance in the Bayesian Framework. Frontiers in Psychology 2019;10:2767. tools:::Rd_expr_doi("10.3389/fpsyg.2019.02767")