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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, ...)

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

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

# S3 method for class 'get_predicted'
map_estimate(
  x,
  precision = 2^10,
  method = "kernel",
  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.

...

Currently not used.

precision

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

method

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

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.

rvar_col

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

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.

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

# \donttest{
library(bayestestR)

posterior <- rnorm(10000)
map_estimate(posterior)
#> MAP Estimate
#> 
#> Parameter | MAP_Estimate
#> ------------------------
#> x         |         0.06

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


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:                0.092 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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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.1e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.2e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds.
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#> Chain 4: 
map_estimate(model)
#> MAP Estimate
#> 
#> Parameter   | MAP_Estimate
#> --------------------------
#> (Intercept) |        39.51
#> wt          |        -3.24
#> cyl         |        -1.39

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 8e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
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map_estimate(model)
#> MAP Estimate
#> 
#> Parameter   | MAP_Estimate
#> --------------------------
#> b_Intercept |        39.67
#> b_wt        |        -3.06
#> b_cyl       |        -1.58
# }