Compute Confidence/Credible/Compatibility Intervals (CI) or Support Intervals (SI) for Bayesian and frequentist models. The Documentation is accessible for:

ci(x, ...) # S3 method for numeric ci(x, ci = 0.89, method = "ETI", verbose = TRUE, BF = 1, ...) # S3 method for data.frame ci(x, ci = 0.89, method = "ETI", verbose = TRUE, BF = 1, ...) # S3 method for sim.merMod ci( x, ci = 0.89, method = "ETI", effects = c("fixed", "random", "all"), parameters = NULL, verbose = TRUE, ... ) # S3 method for sim ci(x, ci = 0.89, method = "ETI", parameters = NULL, verbose = TRUE, ...) # S3 method for stanreg ci( x, ci = 0.89, method = "ETI", effects = c("fixed", "random", "all"), component = c("location", "all", "conditional", "smooth_terms", "sigma", "distributional", "auxiliary"), parameters = NULL, verbose = TRUE, BF = 1, ... ) # S3 method for brmsfit ci( x, ci = 0.89, method = "ETI", effects = c("fixed", "random", "all"), component = c("conditional", "zi", "zero_inflated", "all"), parameters = NULL, verbose = TRUE, BF = 1, ... ) # S3 method for BFBayesFactor ci(x, ci = 0.89, method = "ETI", verbose = TRUE, BF = 1, ...) # S3 method for MCMCglmm ci(x, ci = 0.89, method = "ETI", verbose = TRUE, ...)

x | A |
---|---|

... | Currently not used. |

ci | Value or vector of probability of the CI (between 0 and 1)
to be estimated. Default to |

method | |

verbose | Toggle off warnings. |

BF | The amount of support required to be included in the support interval. |

effects | Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated. |

parameters | Regular expression pattern that describes the parameters that
should be returned. Meta-parameters (like |

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. |

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.`CI`

The probability of the credible interval.`CI_low`

,`CI_high`

The lower and upper credible interval limits for the parameters.

When it comes to interpretation, we recommend thinking of the CI in terms of
an "uncertainty" or "compatibility" interval, the latter being defined as
“Given any value in the interval and the background assumptions,
the data should not seem very surprising” (Gelman & Greenland 2019).

There is also a `plot()`

-method implemented in the see-package.

Gelman A, Greenland S. Are confidence intervals better termed "uncertainty intervals"? BMJ 2019;l5381. doi: 10.1136/bmj.l5381

#> # Equal-Tailed Interval #> #> 89% ETI #> ------------- #> [-1.55, 1.45] #>ci(posterior, method = "HDI")#> # Highest Density Interval #> #> 89% HDI #> ------------- #> [-1.55, 1.45] #>#> # Equal-Tailed Intervals #> #> Parameter | 80% ETI #> ------------------------- #> X1 | [-1.12, 1.42] #> X2 | [-0.93, 1.33] #> X3 | [-1.31, 1.15] #> X4 | [-1.19, 1.42] #> #> #> Parameter | 89% ETI #> ------------------------- #> X1 | [-1.55, 1.50] #> X2 | [-1.17, 1.79] #> X3 | [-1.58, 1.42] #> X4 | [-1.36, 1.60] #> #> #> Parameter | 95% ETI #> ------------------------- #> X1 | [-1.85, 1.73] #> X2 | [-1.75, 2.24] #> X3 | [-1.74, 1.66] #> X4 | [-1.59, 1.74] #> #>#> # Highest Density Intervals #> #> Parameter | 80% HDI #> ------------------------- #> X1 | [-0.83, 1.49] #> X2 | [-0.98, 1.26] #> X3 | [-1.13, 1.27] #> X4 | [-1.22, 1.05] #> #> #> Parameter | 89% HDI #> ------------------------- #> X1 | [-1.23, 1.75] #> X2 | [-1.06, 1.91] #> X3 | [-1.56, 1.43] #> X4 | [-1.26, 1.69] #> #> #> Parameter | 95% HDI #> ------------------------- #> X1 | [-1.86, 1.75] #> X2 | [-1.93, 2.10] #> X3 | [-1.79, 1.67] #> X4 | [-1.59, 1.77] #> #>if (FALSE) { if (require("rstanarm")) { model <- stan_glm(mpg ~ wt, data = mtcars, chains = 2, iter = 200, refresh = 0) ci(model, method = "ETI", ci = c(.80, .89)) ci(model, method = "HDI", ci = c(.80, .89)) ci(model, method = "SI") } if (require("brms")) { model <- brms::brm(mpg ~ wt + cyl, data = mtcars) ci(model, method = "ETI") ci(model, method = "HDI") ci(model, method = "SI") } if (require("BayesFactor")) { bf <- ttestBF(x = rnorm(100, 1, 1)) ci(bf, method = "ETI") ci(bf, method = "HDI") } if (require("emmeans")) { model <- emtrends(model, ~1, "wt") ci(model, method = "ETI") ci(model, method = "HDI") ci(model, method = "SI") } }