Skip to contents

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 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, ...)

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" or "all".

dispersion

Logical, if TRUE, computes indices of dispersion related to the estimate(s) (SD and MAD for mean and median, respectively).

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.

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
# \dontrun{
# rstanarm models
# -----------------------------------------------
library(rstanarm)
model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.052781 seconds (Warm-up)
#> Chain 1:                0.050226 seconds (Sampling)
#> Chain 1:                0.103007 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 2: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 2: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 2: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 2: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.054791 seconds (Warm-up)
#> Chain 2:                0.049115 seconds (Sampling)
#> Chain 2:                0.103906 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 3: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 3: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 3: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 3: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.05239 seconds (Warm-up)
#> Chain 3:                0.059495 seconds (Sampling)
#> Chain 3:                0.111885 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 4: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 4: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 4: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 4: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.055251 seconds (Warm-up)
#> Chain 4:                0.05063 seconds (Sampling)
#> Chain 4:                0.105881 seconds (Total)
#> Chain 4: 
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
# -----------------------------------------------
library(emmeans)
point_estimate(emtrends(model, ~1, "wt"), centrality = c("median", "MAP"))
#> Point Estimate
#> 
#> Parameter | Median |   MAP
#> --------------------------
#> overall   |  -3.19 | -3.19

# brms models
# -----------------------------------------------
library(brms)
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL '2d19b3a372313df641edf05db5e9f303' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.027251 seconds (Warm-up)
#> Chain 1:                0.023965 seconds (Sampling)
#> Chain 1:                0.051216 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '2d19b3a372313df641edf05db5e9f303' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 2: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 2: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 2: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 2: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.028137 seconds (Warm-up)
#> Chain 2:                0.021119 seconds (Sampling)
#> Chain 2:                0.049256 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '2d19b3a372313df641edf05db5e9f303' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 8e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 3: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 3: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 3: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 3: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.025686 seconds (Warm-up)
#> Chain 3:                0.024317 seconds (Sampling)
#> Chain 3:                0.050003 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '2d19b3a372313df641edf05db5e9f303' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 9e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 4: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 4: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 4: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 4: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.026514 seconds (Warm-up)
#> Chain 4:                0.021227 seconds (Sampling)
#> Chain 4:                0.047741 seconds (Total)
#> Chain 4: 
point_estimate(model, centrality = "all", dispersion = TRUE)
#> Point Estimate 
#> 
#> Parameter   | Median |  MAD |  Mean |   SD |   MAP
#> --------------------------------------------------
#> (Intercept) |  39.69 | 1.79 | 39.72 | 1.82 | 39.66
#> wt          |  -3.19 | 0.77 | -3.17 | 0.80 | -3.26
#> cyl         |  -1.51 | 0.43 | -1.52 | 0.44 | -1.46
point_estimate(model, centrality = c("median", "MAP"))
#> Point Estimate 
#> 
#> Parameter   | Median |   MAP
#> ----------------------------
#> (Intercept) |  39.69 | 39.66
#> wt          |  -3.19 | -3.26
#> cyl         |  -1.51 | -1.46

# BayesFactor objects
# -----------------------------------------------
library(BayesFactor)
bf <- 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
# }