Description Usage Arguments Value See Also Examples
View source: R/predictive_interval.R
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
.
1 2 3 4 5 6 7 
object 
Either a fitted model object returned by one of the
rstanarm modeling functions (a stanreg
object) or, for the 
prob 
A number p (0 < p < 1) indicating the desired
probability mass to include in the intervals. The default is to report
90% intervals ( 
newdata, draws, fun, offset, re.form, seed 
Passed to

... 
Currently ignored. 
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 = 1p. For example, if prob=0.9
is
specified (a 90% interval), then the column names will be
"5%"
and "95%"
, respectively.
predictive_error
, posterior_predict
,
posterior_interval
1 2 3 4 5 6 7 8 9 10 11  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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