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, ...)
## 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, ...)
## S3 method for class 'get_predicted'
point_estimate(
x,
centrality = "all",
dispersion = FALSE,
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. |

`...` |
Additional arguments to be passed to or from methods. |

`centrality` |
The point-estimates (centrality indices) to compute. Character
(vector) or list with one or more of these options: |

`dispersion` |
Logical, if |

`threshold` |
For |

`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 |

`use_iterations` |
Logical, if |

`verbose` |
Toggle off warnings. |

### Note

There is also a `plot()`

-method implemented in the see-package.

### References

Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., and Lüdecke, D.
(2019). *Indices of Effect Existence and Significance in the Bayesian Framework*.
Frontiers in Psychology 2019;10:2767. doi:10.3389/fpsyg.2019.02767

### 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"))
# rstanarm models
# -----------------------------------------------
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
# -----------------------------------------------
point_estimate(
emmeans::emtrends(model, ~1, "wt", data = mtcars),
centrality = c("median", "MAP")
)
# brms models
# -----------------------------------------------
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
point_estimate(model, centrality = "all", dispersion = TRUE)
point_estimate(model, centrality = c("median", "MAP"))
# BayesFactor objects
# -----------------------------------------------
bf <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
point_estimate(bf, centrality = "all", dispersion = TRUE)
point_estimate(bf, centrality = c("median", "MAP"))
```

*bayestestR*version 0.14.0 Index]