ci {bayestestR} | R Documentation |
Confidence/Credible/Compatibility Interval (CI)
Description
Compute Confidence/Credible/Compatibility Intervals (CI) or Support Intervals (SI) for Bayesian and frequentist models. The Documentation is accessible for:
Usage
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", BF = 1, rvar_col = NULL, verbose = TRUE, ...)
## S3 method for class 'brmsfit'
ci(
x,
ci = 0.95,
method = "ETI",
effects = "fixed",
component = "conditional",
parameters = NULL,
verbose = TRUE,
BF = 1,
...
)
Arguments
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. |
rvar_col |
A single character - the name of an |
effects |
Should variables for fixed effects ( For models of from packages brms or rstanarm there are additional options:
|
component |
Which type of parameters to return, such as parameters for the conditional model, the zero-inflated part of the model, the dispersion term, etc. See details in section Model Components. May be abbreviated. Note that the conditional component also refers to the count or mean component - names may differ, depending on the modeling package. There are three convenient shortcuts (not applicable to all model classes):
|
parameters |
Regular expression pattern that describes the parameters
that should be returned. Meta-parameters (like |
Details
Value
A data frame with following columns:
-
Parameter
The model parameter(s), ifx
is a model-object. Ifx
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.
Model components
Possible values for the component
argument depend on the model class.
Following are valid options:
-
"all"
: returns all model components, applies to all models, but will only have an effect for models with more than just the conditional model component. -
"conditional"
: only returns the conditional component, i.e. "fixed effects" terms from the model. Will only have an effect for models with more than just the conditional model component. -
"smooth_terms"
: returns smooth terms, only applies to GAMs (or similar models that may contain smooth terms). -
"zero_inflated"
(or"zi"
): returns the zero-inflation component. -
"location"
: returns location parameters such asconditional
,zero_inflated
, orsmooth_terms
(everything that are fixed or random effects - depending on theeffects
argument - but no auxiliary parameters). -
"distributional"
(or"auxiliary"
): components likesigma
,dispersion
,beta
orprecision
(and other auxiliary parameters) are returned.
For models of class brmsfit
(package brms), even more options are
possible for the component
argument, which are not all documented in detail
here. See also ?insight::find_parameters
.
Note
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.
References
Gelman A, Greenland S. Are confidence intervals better termed "uncertainty intervals"? BMJ 2019;l5381. 10.1136/bmj.l5381
See Also
Other ci:
bci()
,
eti()
,
hdi()
,
si()
,
spi()
Examples
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(0.80, 0.89, 0.95))
ci(df, method = "HDI", ci = c(0.80, 0.89, 0.95))
model <- suppressWarnings(rstanarm::stan_glm(
mpg ~ wt,
data = mtcars, chains = 2, iter = 200, refresh = 0
))
ci(model, method = "ETI", ci = c(0.80, 0.89))
ci(model, method = "HDI", ci = c(0.80, 0.89))
bf <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
ci(bf, method = "ETI")
ci(bf, method = "HDI")
model <- emmeans::emtrends(model, ~1, "wt", data = mtcars)
ci(model, method = "ETI")
ci(model, method = "HDI")