rstanarm (version 2.17.4)

predictive_error.stanreg: In-sample or out-of-sample predictive errors

Description

This is a convenience function for computing \(y - y^{rep}\) (in-sample, for observed \(y\)) or \(y - \tilde{y}\) (out-of-sample, for new or held-out \(y\)). The method for stanreg objects calls posterior_predict internally, whereas the method for objects with 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_error(object, newdata = NULL, draws = NULL,
  re.form = NULL, seed = NULL, offset = NULL, ...)
# S3 method for ppd
predictive_error(object, y, ...)

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.

newdata, draws, seed, offset, re.form

Optional arguments passed to posterior_predict. For binomial models, please see the Note section below if newdata will be specified.

...

Currently ignored.

y

For the "ppd" method only, a vector of \(y\) values the same length as the number of columns in the matrix used as object. The method for stanreg objects takes y directly from the fitted model object.

Value

A draws by nrow(newdata) matrix. If newdata is not specified then it will be draws by nobs(object).

See Also

posterior_predict to draw from the posterior predictive distribution without computing predictive errors.

Examples

# NOT RUN {
if (!exists("example_model")) example(example_model)
err1 <- predictive_error(example_model, draws = 50)
hist(err1)

# Using newdata with a binomial model
formula(example_model)
nd <- data.frame(
 size = c(10, 20), 
 incidence = c(5, 10), 
 period = factor(c(1,2)), 
 herd = c(1, 15)
)
err2 <- predictive_error(example_model, newdata = nd, draws = 10, seed = 1234)

# stanreg vs ppd methods
fit <- stan_glm(mpg ~ wt, data = mtcars, iter = 300)
preds <- posterior_predict(fit, seed = 123)
all.equal(
  predictive_error(fit, seed = 123),
  predictive_error(preds, y = fit$y)
)

# }