Compute various point-estimates, such as the mean, the median or the MAP, to describe posterior distributions.
point_estimate(x, ...)# S3 method for numeric
point_estimate(x, centrality = "all", dispersion = FALSE, threshold = 0.1, ...)
# S3 method for data.frame
point_estimate(
x,
centrality = "all",
dispersion = FALSE,
threshold = 0.1,
rvar_col = NULL,
...
)
# 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,
...
)
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.frame
methods.
Additional arguments to be passed to or from methods.
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"
.
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.
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.
A single character - the name of an rvar
column in the data
frame to be processed. See example in p_direction()
.
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
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.
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.
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).
Toggle off warnings.
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")