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Point-estimates of posterior distributions

Source: R/point_estimate.R
point_estimate.Rd

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 '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 class '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 class 'BFBayesFactor'
point_estimate(x, centrality = "all", dispersion = FALSE, ...)

# 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.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.

rvar_col

A single character - the name of an rvar column in the data frame to be processed. See example in p_direction().

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.

Note

There is also a plot()-method implemented in the see-package.

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 2.1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
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#> Chain 1:                0.083 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.2e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.1e-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).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 6e-06 seconds
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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
# }