Compute various point-estimates, such as the mean, the median or the MAP, to describe posterior distributions.
Usage
point_estimate(x, ...)
# S3 method for class 'numeric'
point_estimate(x, centrality = "all", dispersion = FALSE, threshold = 0.1, ...)
# S3 method for class 'data.frame'
point_estimate(
x,
centrality = "all",
dispersion = FALSE,
threshold = 0.1,
rvar_col = NULL,
...
)
# S3 method for class 'brmsfit'
point_estimate(
x,
centrality = "all",
dispersion = FALSE,
effects = "fixed",
component = "conditional",
parameters = NULL,
...
)
# S3 method for class '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.frame
methods.- ...
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"
(seemap_estimate()
),"trimmed"
(which is justmean(x, trim = threshold)
),"mode"
or"all"
.- dispersion
Logical, if
TRUE
, computes indices of dispersion related to the estimate(s) (SD
andMAD
formean
andmedian
, 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.- rvar_col
A single character - the name of an
rvar
column in the data frame to be processed. See example inp_direction()
.- effects
Should results for fixed effects (
"fixed"
, the default), random effects ("random"
) or both ("all"
) be returned? Only applies to mixed models. May be abbreviated.- component
Which type of parameters to return, such as parameters for the conditional model, the zero-inflated part of the model, the dispersion term, etc. See details in section Model Components. May be abbreviated. Note that the conditional component also refers to the count or mean component - names may differ, depending on the modeling package. There are three convenient shortcuts (not applicable to all model classes):
component = "all"
returns all possible parameters.If
component = "location"
, location parameters such asconditional
,zero_inflated
,smooth_terms
, orinstruments
are returned (everything that are fixed or random effects - depending on theeffects
argument - but no auxiliary parameters).For
component = "distributional"
(or"auxiliary"
), components likesigma
,dispersion
,beta
orprecision
(and other auxiliary parameters) are returned.
- parameters
Regular expression pattern that describes the parameters that should be returned. Meta-parameters (like
lp__
orprior_
) are filtered by default, so only parameters that typically appear in thesummary()
are returned. Useparameters
to select specific parameters for the output.- use_iterations
Logical, if
TRUE
andx
is aget_predicted
object, (returned byinsight::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.
Note
There is also a plot()
-method implemented in the see-package.
Model components
Possible values for the component
argument depend on the model class.
Following are valid options:
"all"
: returns all model components, applies to all models, but will only have an effect for models with more than just the conditional model component."conditional"
: only returns the conditional component, i.e. "fixed effects" terms from the model. Will only have an effect for models with more than just the conditional model component."smooth_terms"
: returns smooth terms, only applies to GAMs (or similar models that may contain smooth terms)."zero_inflated"
(or"zi"
): returns the zero-inflation component."location"
: returns location parameters such asconditional
,zero_inflated
, orsmooth_terms
(everything that are fixed or random effects - depending on theeffects
argument - but no auxiliary parameters)."distributional"
(or"auxiliary"
): components likesigma
,dispersion
,beta
orprecision
(and other auxiliary parameters) are returned.
For models of class brmsfit
(package brms), even more options are
possible for the component
argument, which are not all documented in detail
here. See also ?insight::find_parameters
.
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. doi:10.3389/fpsyg.2019.02767
Examples
library(bayestestR)
point_estimate(rnorm(1000))
#> Point Estimate
#>
#> Median | Mean | MAP
#> ----------------------
#> 5.58e-03 | 0.01 | 0.03
point_estimate(rnorm(1000), centrality = "all", dispersion = TRUE)
#> Point Estimate
#>
#> Median | MAD | Mean | SD | MAP
#> ---------------------------------------
#> -0.02 | 0.97 | -5.33e-03 | 1.00 | 0.05
point_estimate(rnorm(1000), centrality = c("median", "MAP"))
#> Point Estimate
#>
#> Median | MAP
#> --------------
#> 0.03 | -0.07
df <- data.frame(replicate(4, rnorm(100)))
point_estimate(df, centrality = "all", dispersion = TRUE)
#> Point Estimate
#>
#> Parameter | Median | MAD | Mean | SD | MAP
#> ------------------------------------------------
#> X1 | -0.02 | 1.21 | 0.02 | 1.12 | 0.85
#> X2 | -0.07 | 1.07 | -0.10 | 1.00 | -0.18
#> X3 | -0.29 | 1.02 | -0.33 | 0.89 | -0.10
#> X4 | -0.08 | 0.90 | -0.14 | 0.89 | 0.11
point_estimate(df, centrality = c("median", "MAP"))
#> Point Estimate
#>
#> Parameter | Median | MAP
#> --------------------------
#> X1 | -0.02 | 0.85
#> X2 | -0.07 | -0.18
#> X3 | -0.29 | -0.10
#> X4 | -0.08 | 0.11
# \donttest{
# rstanarm models
# -----------------------------------------------
model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 2.2e-05 seconds
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point_estimate(model, centrality = "all", dispersion = TRUE)
#> Point Estimate
#>
#> Parameter | Median | MAD | Mean | SD | MAP
#> --------------------------------------------------
#> (Intercept) | 39.65 | 1.86 | 39.69 | 1.84 | 39.57
#> wt | -3.19 | 0.81 | -3.18 | 0.82 | -3.19
#> cyl | -1.51 | 0.44 | -1.52 | 0.44 | -1.54
point_estimate(model, centrality = c("median", "MAP"))
#> Point Estimate
#>
#> Parameter | Median | MAP
#> ----------------------------
#> (Intercept) | 39.65 | 39.57
#> wt | -3.19 | -3.19
#> cyl | -1.51 | -1.54
# emmeans estimates
# -----------------------------------------------
point_estimate(
emmeans::emtrends(model, ~1, "wt", data = mtcars),
centrality = c("median", "MAP")
)
#> Point Estimate
#>
#> X1 | Median | MAP
#> ------------------------
#> overall | -3.19 | -3.19
# brms models
# -----------------------------------------------
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
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point_estimate(model, centrality = "all", dispersion = TRUE)
#> Point Estimate
#>
#> Parameter | Median | MAD | Mean | SD | MAP
#> --------------------------------------------------
#> (Intercept) | 39.67 | 1.71 | 39.67 | 1.78 | 39.86
#> wt | -3.22 | 0.78 | -3.20 | 0.80 | -3.32
#> cyl | -1.49 | 0.44 | -1.50 | 0.43 | -1.46
point_estimate(model, centrality = c("median", "MAP"))
#> Point Estimate
#>
#> Parameter | Median | MAP
#> ----------------------------
#> (Intercept) | 39.67 | 39.86
#> wt | -3.22 | -3.32
#> cyl | -1.49 | -1.46
# BayesFactor objects
# -----------------------------------------------
bf <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
point_estimate(bf, centrality = "all", dispersion = TRUE)
#> Point Estimate
#>
#> Parameter | Median | MAD | Mean | SD | MAP
#> -----------------------------------------------
#> Difference | 1.03 | 0.11 | 1.03 | 0.11 | 1.02
point_estimate(bf, centrality = c("median", "MAP"))
#> Point Estimate
#>
#> Parameter | Median | MAP
#> --------------------------
#> Difference | 1.03 | 1.04
# }