spi {bayestestR} | R Documentation |
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.
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,
...
)
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 zero-inflated part of the model be returned? May be abbreviated. Only applies to brms-models. |
parameters |
Regular expression pattern that describes the parameters
that should be returned. Meta-parameters (like |
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 (see also discussion
here).
A data frame with following columns:
Parameter
The model parameter(s), if x
is a model-object. 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.
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.
Liu, Y., Gelman, A., & Zheng, T. (2015). Simulation-efficient shortest probability intervals. Statistics and Computing, 25(4), 809–819. https://doi.org/10.1007/s11222-015-9563-8
Other ci:
bci()
,
ci()
,
cwi()
,
eti()
,
hdi()
,
si()
library(bayestestR)
posterior <- rnorm(1000)
spi(posterior)
spi(posterior, ci = c(.80, .89, .95))
df <- data.frame(replicate(4, rnorm(100)))
spi(df)
spi(df, ci = c(.80, .89, .95))
## Not run:
library(rstanarm)
model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200, refresh = 0)
spi(model)
## End(Not run)