rstanarm (version 2.17.4)

# 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 stanreg
predictive_interval(object, prob = 0.9, newdata = NULL,
draws = NULL, re.form = NULL, fun = NULL, seed = NULL,
offset = NULL, ...)# S3 method for 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 $$p \in (0,1)$$ 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 $$100\alpha/2$$% and $$100(1 - \alpha/2)$$%, where $$\alpha = 1-p$$. For example, if prob=0.9 is specified (a $$90$$% interval), then the column names will be "5%" and "95%", respectively.

## See Also

predictive_error, posterior_predict, posterior_interval

## Examples

Run this code
# NOT RUN {
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)
)

# }


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