ci {bayestestR}  R Documentation 
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 class 'numeric' ci(x, ci = 0.95, method = "ETI", verbose = TRUE, BF = 1, ...) ## S3 method for class 'data.frame' ci(x, ci = 0.95, method = "ETI", verbose = TRUE, BF = 1, ...) ## S3 method for class 'sim.merMod' ci( x, ci = 0.95, method = "ETI", effects = c("fixed", "random", "all"), parameters = NULL, verbose = TRUE, ... ) ## S3 method for class 'sim' ci(x, ci = 0.95, method = "ETI", parameters = NULL, verbose = TRUE, ...) ## S3 method for class 'stanreg' ci( x, ci = 0.95, 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 class 'brmsfit' ci( x, ci = 0.95, method = "ETI", effects = c("fixed", "random", "all"), component = c("conditional", "zi", "zero_inflated", "all"), parameters = NULL, verbose = TRUE, BF = 1, ... ) ## S3 method for class 'BFBayesFactor' ci(x, ci = 0.95, method = "ETI", verbose = TRUE, BF = 1, ...) ## S3 method for class 'MCMCglmm' ci(x, ci = 0.95, 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. Metaparameters (like 
component 
Should results for all parameters, parameters for the conditional model or the zeroinflated part of the model be returned? May be abbreviated. Only applies to brmsmodels. 
A data frame with following columns:
Parameter
The model parameter(s), if x
is a modelobject. 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 seepackage.
Gelman A, Greenland S. Are confidence intervals better termed "uncertainty intervals"? BMJ 2019;l5381. doi: 10.1136/bmj.l5381
library(bayestestR) posterior < rnorm(1000) ci(posterior, method = "ETI") ci(posterior, method = "HDI") df < data.frame(replicate(4, rnorm(100))) ci(df, method = "ETI", ci = c(.80, .89, .95)) ci(df, method = "HDI", ci = c(.80, .89, .95)) ## Not run: 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") } ## End(Not run)