spi {bayestestR}  R Documentation 
Shortest Probability Interval (SPI)
Description
Compute the Shortest Probability Interval (SPI) of posterior distributions. The SPI is a more computationally stable HDI. The implementation is based on the algorithm from the SPIn package.
Usage
spi(x, ...)
## S3 method for class 'numeric'
spi(x, ci = 0.95, verbose = TRUE, ...)
## S3 method for class 'stanreg'
spi(
x,
ci = 0.95,
effects = c("fixed", "random", "all"),
component = c("location", "all", "conditional", "smooth_terms", "sigma",
"distributional", "auxiliary"),
parameters = NULL,
verbose = TRUE,
...
)
## S3 method for class 'brmsfit'
spi(
x,
ci = 0.95,
effects = c("fixed", "random", "all"),
component = c("conditional", "zi", "zero_inflated", "all"),
parameters = NULL,
verbose = TRUE,
...
)
## S3 method for class 'get_predicted'
spi(x, ci = 0.95, use_iterations = FALSE, verbose = TRUE, ...)
Arguments
x 
Vector representing a posterior distribution, or a data frame of such
vectors. Can also be a Bayesian model. bayestestR supports a wide range
of models (see, for example, 
... 
Currently not used. 
ci 
Value or vector of probability of the (credible) interval  CI
(between 0 and 1) to be estimated. Default to 
verbose 
Toggle off warnings. 
effects 
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated. 
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. 
parameters 
Regular expression pattern that describes the parameters
that should be returned. Metaparameters (like 
use_iterations 
Logical, if 
Details
The SPI is an alternative method to the HDI (hdi()
) to quantify
uncertainty of (posterior) distributions. The SPI is said to be more stable
than the HDI, because, the "HDI can be noisy (that is, have a high Monte Carlo error)"
(Liu et al. 2015). Furthermore, the HDI is sensitive to additional assumptions,
in particular assumptions related to the different estimation methods, which
can make the HDI less accurate or reliable.
Value
A data frame with following columns:

Parameter
The model parameter(s), ifx
is a modelobject. 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.
Note
The code to compute the SPI was adapted from the SPIn package, and slightly modified to be more robust for Stan models. Thus, credits go to Ying Liu for the original SPI algorithm and R implementation.
References
Liu, Y., Gelman, A., & Zheng, T. (2015). Simulationefficient shortest probability intervals. Statistics and Computing, 25(4), 809–819. https://doi.org/10.1007/s1122201595638
See Also
Other ci:
bci()
,
ci()
,
cwi()
,
eti()
,
hdi()
,
si()
Examples
library(bayestestR)
posterior < rnorm(1000)
spi(posterior)
spi(posterior, ci = c(0.80, 0.89, 0.95))
df < data.frame(replicate(4, rnorm(100)))
spi(df)
spi(df, ci = c(0.80, 0.89, 0.95))
library(rstanarm)
model < suppressWarnings(
stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
)
spi(model)