rstanarm: predictive_interval.stanreg – R documentation

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predictive_interval.stanreg

Predictive intervals

Description

For models fit using MCMC (algorithm="sampling") or one of the variational approximations ("meanfield" or "fullrank"), the predictive_interval function computes Bayesian predictive intervals. The method for stanreg objects calls posterior_predict internally, whereas the method for objects of class "ppd" accepts the matrix returned by posterior_predict as input and can be used to avoid multiple calls to posterior_predict.

Usage

## S3 method for class 'stanreg'
predictive_interval(
  object,
  prob = 0.9,
  newdata = NULL,
  draws = NULL,
  re.form = NULL,
  fun = NULL,
  seed = NULL,
  offset = NULL,
  ...
)

## S3 method for class 'ppd'
predictive_interval(object, prob = 0.9, ...)

Arguments

`object`	Either a fitted model object returned by one of the rstanarm modeling functions (a stanreg object) or, for the `"ppd"` method, a matrix of draws from the posterior predictive distribution returned by `posterior_predict`.
`prob`	A number p (0 < p < 1) indicating the desired probability mass to include in the intervals. The default is to report 90% intervals (`prob=0.9`) rather than the traditionally used 95% (see Details).
`newdata, draws, fun, offset, re.form, seed`	Passed to `posterior_predict`.
`...`	Currently ignored.

Value

A matrix with two columns and as many rows as are in newdata. If newdata is not provided then the matrix will have as many rows as the data used to fit the model. For a given value of prob, p, the columns correspond to the lower and upper 100p% central 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 will be "5%" and "95%", respectively.

Examples

fit <- stan_glm(mpg ~ wt, data = mtcars, iter = 300)
predictive_interval(fit)
predictive_interval(fit, newdata = data.frame(wt = range(mtcars$wt)), 
                    prob = 0.5)

# stanreg vs ppd methods
preds <- posterior_predict(fit, seed = 123)
all.equal(
  predictive_interval(fit, seed = 123),
  predictive_interval(preds)
)

rstanarm

Bayesian Applied Regression Modeling via Stan

v2.21.1

GPL (>= 3)

Authors

Jonah Gabry [aut], Imad Ali [ctb], Sam Brilleman [ctb], Jacqueline Buros Novik [ctb] (R/stan_jm.R), AstraZeneca [ctb] (R/stan_jm.R), Trustees of Columbia University [cph], Simon Wood [cph] (R/stan_gamm4.R), R Core Deveopment Team [cph] (R/stan_aov.R), Douglas Bates [cph] (R/pp_data.R), Martin Maechler [cph] (R/pp_data.R), Ben Bolker [cph] (R/pp_data.R), Steve Walker [cph] (R/pp_data.R), Brian Ripley [cph] (R/stan_aov.R, R/stan_polr.R), William Venables [cph] (R/stan_polr.R), Paul-Christian Burkner [cph] (R/misc.R), Ben Goodrich [cre, aut]

Initial release

2020-07-20