map_estimate {bayestestR} 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, precision = 2^10, method = "kernel", ...)

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

## S3 method for class 'bayesQR'
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 'emmGrid'
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 (`stanreg`, `brmsfit`, `MCMCglmm`, `mcmc` or `bcplm`) or a `BayesFactor` model. `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 `posterior` is a vector. If `posterior` 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

```## Not run:
library(bayestestR)

posterior <- rnorm(10000)
map_estimate(posterior)

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

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

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

## End(Not run)

```

[Package bayestestR version 0.10.0 Index]