R: Generic function and default method for posterior uncertainty...

posterior_interval {rstantools} | R Documentation

## Generic function and default method for posterior uncertainty intervals

### Description

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. See `posterior_interval.stanreg` in the rstanarm package for an example.

### Usage

    posterior_interval(object, ...)

    ## Default S3 method:
    posterior_interval(object, prob = 0.9, ...)

### Arguments

`object` | The object to use.
`...` | Arguments passed to methods. See the methods in the rstanarm package for examples.
`prob` | A number p (0 < p < 1) indicating the desired probability mass to include in the intervals.

### Value

`posterior_interval` methods should return a matrix with two columns and as many rows as model parameters (or a subset of parameters specified by the user). 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 = 1-p. For example, if `prob=0.9` is specified (a 90% interval), then the column names would be `"5%"` and `"95%"`, respectively.

The default method just takes `object` to be a matrix (one column per parameter) and computes quantiles, with `prob` defaulting to 0.9.

### See Also

- The rstanarm package for example methods (CRAN, GitHub).

- Guidelines and recommendations for developers of R packages interfacing with Stan and a demonstration getting a simple package working can be found in the vignettes included with rstantools and at http://mc-stan.org/rstantools/articles/.

### Examples

    # Default method takes a numeric matrix (of posterior draws)
    draws <- matrix(rnorm(100 * 5), 100, 5) # fake draws
    colnames(draws) <- paste0("theta_", 1:5)
    posterior_interval(draws)

    # Example using rstanarm package:
    # posterior_interval method for 'stanreg' objects
    ## Not run:
    if (require("rstanarm")) {
      fit <- stan_glmer(
        mpg ~ wt + am + (1|cyl),
        data = mtcars,
        QR = TRUE,
        prior = normal(0, 1),
        iter = 500 # to speed up example
      )
      rstanarm::posterior_interval(fit, prob = 0.5)
    }

    ## End(Not run)

    # Also see help("posterior_interval", package = "rstanarm")

[Package rstantools version 1.5.0 Index]