A support interval contains only the values of the parameter that predict the observed data better than average, by some degree k; these are values of the parameter that are associated with an updating factor greater or equal than k. From the perspective of the Savage-Dickey Bayes factor, testing against a point null hypothesis for any value within the support interval will yield a Bayes factor smaller than 1/k.
si(posterior, ...)# S3 method for numeric
si(posterior, prior = NULL, BF = 1, verbose = TRUE, ...)
# S3 method for stanreg
si(
posterior,
prior = NULL,
BF = 1,
verbose = TRUE,
effects = c("fixed", "random", "all"),
component = c("location", "conditional", "all", "smooth_terms", "sigma", "auxiliary",
"distributional"),
parameters = NULL,
...
)
# S3 method for brmsfit
si(
posterior,
prior = NULL,
BF = 1,
verbose = TRUE,
effects = c("fixed", "random", "all"),
component = c("location", "conditional", "all", "smooth_terms", "sigma", "auxiliary",
"distributional"),
parameters = NULL,
...
)
# S3 method for blavaan
si(
posterior,
prior = NULL,
BF = 1,
verbose = TRUE,
effects = c("fixed", "random", "all"),
component = c("location", "conditional", "all", "smooth_terms", "sigma", "auxiliary",
"distributional"),
parameters = NULL,
...
)
# S3 method for emmGrid
si(posterior, prior = NULL, BF = 1, verbose = TRUE, ...)
# S3 method for get_predicted
si(
posterior,
prior = NULL,
BF = 1,
use_iterations = FALSE,
verbose = TRUE,
...
)
# S3 method for data.frame
si(posterior, prior = NULL, BF = 1, rvar_col = NULL, verbose = TRUE, ...)
A data frame containing the lower and upper bounds of the SI.
Note that if the level of requested support is higher than observed in the data, the
interval will be [NA,NA].
A numerical vector, stanreg / brmsfit object,
emmGrid or a data frame - representing a posterior distribution(s)
from (see 'Details').
Arguments passed to and from other methods. (Can be used to pass
arguments to internal logspline::logspline().)
An object representing a prior distribution (see 'Details').
The amount of support required to be included in the support interval.
Toggle off warnings.
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
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.
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.
Logical, if TRUE and x is a get_predicted object,
(returned by insight::get_predicted()), the function is applied to the
iterations instead of the predictions. This only applies to models that return
iterations for predicted values (e.g., brmsfit models).
A single character - the name of an rvar column in the data
frame to be processed. See example in p_direction().
The choice of BF (the level of support) depends on what we want our interval
to represent:
A BF = 1 contains values whose credibility is not decreased by observing the data.
A BF > 1 contains values who received more impressive support from the data.
A BF < 1 contains values whose credibility has not been impressively
decreased by observing the data. Testing against values outside this interval
will produce a Bayes factor larger than 1/BF in support of the alternative.
E.g., if an SI (BF = 1/3) excludes 0, the Bayes factor against the point-null
will be larger than 3.
For the computation of Bayes factors, the model priors must be proper priors
(at the very least they should be not flat, and it is preferable that
they be informative); As the priors for the alternative get wider, the
likelihood of the null value(s) increases, to the extreme that for completely
flat priors the null is infinitely more favorable than the alternative (this
is called the Jeffreys-Lindley-Bartlett paradox). Thus, you should
only ever try (or want) to compute a Bayes factor when you have an informed
prior.
(Note that by default, brms::brm() uses flat priors for fixed-effects;
See example below.)
It is important to provide the correct prior for meaningful results,
to match the posterior-type input:
A numeric vector - prior should also be a numeric vector, representing the prior-estimate.
A data frame - prior should also be a data frame, representing the prior-estimates, in matching column order.
If rvar_col is specified, prior should be the name of an rvar column that represents the prior-estimates.
Supported Bayesian model (stanreg, brmsfit, etc.)
prior should be a model an equivalent model with MCMC samples from the priors only. See unupdate().
If prior is set to NULL, unupdate() is called internally (not supported for brmsfit_multiple model).
Output from a {marginaleffects} function - prior should also be an equivalent output from a {marginaleffects} function based on a prior-model
(See unupdate()).
Output from an {emmeans} function
prior should also be an equivalent output from an {emmeans} function based on a prior-model (See unupdate()).
prior can also be the original (posterior) model, in which case the function
will try to "unupdate" the estimates (not supported if the estimates have undergone
any transformations -- "log", "response", etc. -- or any regriding).
For more info, in particular on specifying correct priors for factors with more than 2 levels, see the Bayes factors vignette.
This method is used to compute support intervals based on prior and posterior distributions.
For the computation of support intervals, the model priors must be proper priors (at the very least
they should be not flat, and it is preferable that they be informative - note
that by default, brms::brm() uses flat priors for fixed-effects; see example below).
Wagenmakers, E., Gronau, Q. F., Dablander, F., & Etz, A. (2018, November 22). The Support Interval. tools:::Rd_expr_doi("10.31234/osf.io/zwnxb")
Other ci:
bci(),
ci(),
eti(),
hdi(),
spi()