point_estimate {bayestestR} R Documentation

## Point-estimates of posterior distributions

### Description

Compute various point-estimates, such as the mean, the median or the MAP, to describe posterior distributions.

### Usage

```point_estimate(x, centrality = "all", dispersion = FALSE, ...)

## S3 method for class 'numeric'
point_estimate(x, centrality = "all", dispersion = FALSE, threshold = 0.1, ...)

## S3 method for class '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 class '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 class '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 (`stanreg`, `brmsfit`, `MCMCglmm`, `mcmc` or `bcplm`) or a `BayesFactor` model. `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). `...` Additional arguments to be passed to or from methods. `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.

### Examples

```library(bayestestR)

point_estimate(rnorm(1000))
point_estimate(rnorm(1000), centrality = "all", dispersion = TRUE)
point_estimate(rnorm(1000), centrality = c("median", "MAP"))

df <- data.frame(replicate(4, rnorm(100)))
point_estimate(df, centrality = "all", dispersion = TRUE)
point_estimate(df, centrality = c("median", "MAP"))
## Not run:
# rstanarm models
# -----------------------------------------------
library(rstanarm)
model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)
point_estimate(model, centrality = "all", dispersion = TRUE)
point_estimate(model, centrality = c("median", "MAP"))

# emmeans estimates
# -----------------------------------------------
library(emmeans)
point_estimate(emtrends(model, ~1, "wt"), centrality = c("median", "MAP"))

# brms models
# -----------------------------------------------
library(brms)
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
point_estimate(model, centrality = "all", dispersion = TRUE)
point_estimate(model, centrality = c("median", "MAP"))

# BayesFactor objects
# -----------------------------------------------
library(BayesFactor)
bf <- ttestBF(x = rnorm(100, 1, 1))
point_estimate(bf, centrality = "all", dispersion = TRUE)
point_estimate(bf, centrality = c("median", "MAP"))

## End(Not run)

```

[Package bayestestR version 0.10.0 Index]