# map_estimate: Maximum A Posteriori probability estimate (MAP) In easystats/bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

 map_estimate R Documentation

## Maximum A Posteriori probability estimate (MAP)

### Description

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", ...)

## 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.frame`methods. `...` 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. `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

``````

library(bayestestR)

posterior <- rnorm(10000)
map_estimate(posterior)

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

model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)
map_estimate(model)

model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
map_estimate(model)

``````

easystats/bayestestR documentation built on Aug. 1, 2024, 9:41 a.m.