Description Usage Arguments Details Value References See Also Examples
View source: R/posterior_interval.R
For models fit using MCMC (algorithm="sampling"
) or one of the
variational approximations ("meanfield"
or "fullrank"
), the
posterior_interval
function computes Bayesian posterior uncertainty
intervals. These intervals are often referred to as credible
intervals, but we use the term uncertainty intervals to highlight the
fact that wider intervals correspond to greater uncertainty.
1 2 3  ## S3 method for class 'stanreg'
posterior_interval(object, prob = 0.9,
type = "central", pars = NULL, regex_pars = NULL, ...)

object 
A fitted model object returned by one of the
rstanarm modeling functions. See 
prob 
A number p (0 < p < 1) indicating the desired
probability mass to include in the intervals. The default is to report
90% intervals ( 
type 
The type of interval to compute. Currently the only option is

pars 
An optional character vector of parameter names. 
regex_pars 
An optional character vector of regular
expressions to use for parameter selection. 
... 
Currently ignored. 
Unlike for a frenquentist confidence interval, it is valid to say that, conditional on the data and model, we believe that with probability p the value of a parameter is in its 100p% posterior interval. This intuitive interpretation of Bayesian intervals is often erroneously applied to frequentist confidence intervals. See Morey et al. (2015) for more details on this issue and the advantages of using Bayesian posterior uncertainty intervals (also known as credible intervals).
We default to reporting 90% intervals rather than 95% intervals for several reasons:
Computational stability: 90% intervals are more stable than 95% intervals (for which each end relies on only 2.5% of the posterior draws).
Relation to TypeS errors (Gelman and Carlin, 2014): 95% of the mass in a 90% central interval is above the lower value (and 95% is below the upper value). For a parameter θ, it is therefore easy to see if the posterior probability that θ > 0 (or θ < 0) is larger or smaller than 95%.
Of course, if 95% intervals are desired they can be computed by
specifying prob=0.95
.
Currently posterior_interval
only computes central intervals because
other types of intervals are rarely useful for the models that rstanarm
can estimate. Additional possibilities may be provided in future releases as
more models become available.
A matrix with two columns and as many rows as model parameters (or
the subset of parameters specified by pars
and/or
regex_pars
). For a given value of prob
, p, the columns
correspond to the lower and upper 100p% interval limits and have the
names 100α/2% and 100(1  α/2)%, where α
= 1p. For example, if prob=0.9
is specified (a 90%
interval), then the column names will be "5%"
and "95%"
,
respectively.
Gelman, A. and Carlin, J. (2014). Beyond power calculations: assessing Type S (sign) and Type M (magnitude) errors. Perspectives on Psychological Science. 9(6), 641–51.
Morey, R. D., Hoekstra, R., Rouder, J., Lee, M. D., and Wagenmakers, E. (2016). The fallacy of placing confidence in confidence intervals. Psychonomic Bulletin & Review. 23(1), 103–123.
confint.stanreg
, which, for models fit using optimization, can
be used to compute traditional confidence intervals.
predictive_interval
for predictive intervals.
1 2 3 4  if (!exists("example_model")) example(example_model)
posterior_interval(example_model)
posterior_interval(example_model, regex_pars = "herd")
posterior_interval(example_model, pars = "period2", prob = 0.5)

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