rstanarm: predictive_error.stanreg – R documentation

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predictive_error.stanreg

In-sample or out-of-sample predictive errors

Description

This is a convenience function for computing y - yrep (in-sample, for observed y) or y - ytilde (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 class 'stanreg'
predictive_error(
  object,
  newdata = NULL,
  draws = NULL,
  re.form = NULL,
  seed = NULL,
  offset = NULL,
  ...
)

## S3 method for class '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).

Note

The Note section in posterior_predict about newdata for binomial models also applies for predictive_error, with one important difference. For posterior_predict if the left-hand side of the model formula is cbind(successes, failures) then the particular values of successes and failures in newdata don't matter, only that they add to the desired number of trials. This is not the case for predictive_error. For predictive_error the particular value of successes matters because it is used as y when computing the error.

Examples

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)
)

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