loo (version 1.1.0)

waic: Widely applicable information criterion (WAIC)

Description

Widely applicable information criterion (WAIC)

Usage

waic(x, ...)
# S3 method for matrix
waic(x, ...)
# S3 method for function
waic(x, ..., args)

Arguments

x

A log-likelihood matrix or function. See the Methods (by class) section below for a detailed description.

...

Other arguments. Currently ignored.

args

Only required if x is a function. A list containing the data required to specify the arguments to the function. See the Methods (by class) section below for how args should be specified.

Value

A named list (of class 'loo') with components:

elpd_waic, se_elpd_waic

expected log pointwise predictive density and standard error

p_waic, se_p_waic

estimated effective number of parameters and standard error

waic, se_waic

-2 * elpd_waic (i.e., converted to the deviance scale) and standard error

pointwise

the pointwise contributions of each of the above measures

Methods (by class)

• matrix: An \(S\) by \(N\) matrix, where \(S\) is the size of the posterior sample (the number of simulations) and \(N\) is the number of data points. For example, the object returned by extract_log_lik can be used.

• function: A function \(f\) that takes arguments i, data, and draws and returns a vector containing the log-likelihood for the ith observation evaluated at each posterior draw.

  The args argument must also be specified and should be a named list with the following components:

  • draws: An object containing the posterior draws for any parameters needed to compute the pointwise log-likelihood.

  • data: An object containing data (e.g. observed outcome and predictors) needed to compute the pointwise log-likelihood. data should be in an appropriate form so that \(f\)(i=i, data=data[i,,drop=FALSE], draws=draws) returns the S-vector of log-likelihoods for the ith observation.

  • N: The number of observations.

  • S: The size of the posterior sample.

See Also

compare, print.loo, loo-package

Examples

# NOT RUN {
log_lik1 <- extract_log_lik(stanfit1)
log_lik2 <- extract_log_lik(stanfit2)
(waic1 <- waic(log_lik1))
(waic2 <- waic(log_lik2))
print(compare(waic1, waic2), digits = 2)
# }
# NOT RUN {
# }