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Maximum A Posteriori probability estimate (MAP)

Source: R/map_estimate.R
map_estimate.Rd

Find the Highest Maximum A Posteriori probability estimate (MAP) of a posterior, i.e., the value associated with the highest probability density (the "peak" of the posterior distribution). In other words, it is an estimation of the mode for continuous parameters. Note that this function relies on estimate_density, which by default uses a different smoothing bandwidth ("SJ") compared to the legacy default implemented the base R density function ("nrd0").

Usage

map_estimate(x, precision = 2^10, method = "kernel", ...)

# S3 method for numeric
map_estimate(x, precision = 2^10, method = "kernel", ...)

# S3 method for stanreg
map_estimate(
  x,
  precision = 2^10,
  method = "kernel",
  effects = c("fixed", "random", "all"),
  component = c("location", "all", "conditional", "smooth_terms", "sigma",
    "distributional", "auxiliary"),
  parameters = NULL,
  ...
)

# S3 method for brmsfit
map_estimate(
  x,
  precision = 2^10,
  method = "kernel",
  effects = c("fixed", "random", "all"),
  component = c("conditional", "zi", "zero_inflated", "all"),
  parameters = NULL,
  ...
)

# S3 method for data.frame
map_estimate(x, precision = 2^10, method = "kernel", ...)

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.

precision

Number of points of density data. See the n parameter in density.

method

Density estimation method. Can be "kernel" (default), "logspline" or "KernSmooth".

...

Currently not used.

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.

Value

A numeric value if x is a vector. If x is a model-object, returns a data frame with following columns:

  • Parameter The model parameter(s), if x is a model-object. If x is a vector, this column is missing.

  • MAP_Estimate The MAP estimate for the posterior or each model parameter.

Examples

# \dontrun{
library(bayestestR)

posterior <- rnorm(10000)
map_estimate(posterior)
#> MAP Estimate: 0.03

plot(density(posterior))
abline(v = map_estimate(posterior), col = "red")


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 2.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
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#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
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#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
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map_estimate(model)
#> MAP Estimate
#> 
#> Parameter   | MAP_Estimate
#> --------------------------
#> (Intercept) |        39.67
#> wt          |        -3.11
#> cyl         |        -1.52

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 1.2e-05 seconds
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map_estimate(model)
#> MAP Estimate
#> 
#> Parameter   | MAP_Estimate
#> --------------------------
#> b_Intercept |        39.58
#> b_wt        |        -3.24
#> b_cyl       |        -1.45
# }