Efficient approximate leave-one-out cross-validation (LOO) for posterior approximations
Efficient approximate leave-one-out cross-validation (LOO) for posterior approximations
loo_approximate_posterior(x, log_p, log_g, ...)
## S3 method for class 'array'
loo_approximate_posterior(
x,
log_p,
log_g,
...,
save_psis = FALSE,
cores = getOption("mc.cores", 1)
)
## S3 method for class 'matrix'
loo_approximate_posterior(
x,
log_p,
log_g,
...,
save_psis = FALSE,
cores = getOption("mc.cores", 1)
)
## S3 method for class ''function''
loo_approximate_posterior(
x,
...,
data = NULL,
draws = NULL,
log_p = NULL,
log_g = NULL,
save_psis = FALSE,
cores = getOption("mc.cores", 1)
)x |
A log-likelihood array, matrix, or function. The Methods (by class) section, below, has detailed descriptions of how to specify the inputs for each method. |
log_p |
The log-posterior (target) evaluated at S samples from the proposal distribution (g). A vector of length S. |
log_g |
The log-density (proposal) evaluated at S samples from the proposal distribution (g). A vector of length S. |
save_psis |
Should the |
cores |
The number of cores to use for parallelization. This defaults to
the option
|
data, draws, ... |
For the |
The loo_approximate_posterior() function is an S3 generic and
methods are provided for 3-D pointwise log-likelihood arrays, pointwise
log-likelihood matrices, and log-likelihood functions. The implementation
works for posterior approximations where it is possible to compute the log
density for the posterior approximation.
The loo_approximate_posterior() methods return a named list with
class c("psis_loo_ap", "psis_loo", "loo"). It has the same structure
as the objects returned by loo() but with the additional slot:
posterior_approximationA list with two vectors, log_p and log_g of the same length
containing the posterior density and the approximation density
for the individual draws.
array: An I by C by N array, where I
is the number of MCMC iterations per chain, C is the number of
chains, and N is the number of data points.
matrix: An S by N matrix, where S is the size
of the posterior sample (with all chains merged) and N is the number
of data points.
function: A function f() that takes arguments data_i and draws and returns a
vector containing the log-likelihood for a single observation i evaluated
at each posterior draw. The function should be written such that, for each
observation i in 1:N, evaluating
f(data_i = data[i,, drop=FALSE], draws = draws)
results in a vector of length S (size of posterior sample). The
log-likelihood function can also have additional arguments but data_i and
draws are required.
If using the function method then the arguments data and draws must also
be specified in the call to loo():
data: A data frame or matrix containing the data (e.g.
observed outcome and predictors) needed to compute the pointwise
log-likelihood. For each observation i, the ith row of
data will be passed to the data_i argument of the
log-likelihood function.
draws: An object containing the posterior draws for any
parameters needed to compute the pointwise log-likelihood. Unlike
data, which is indexed by observation, for each observation the
entire object draws will be passed to the draws argument of
the log-likelihood function.
The ... can be used if your log-likelihood function takes additional
arguments. These arguments are used like the draws argument in that they
are recycled for each observation.
Magnusson, M., Riis Andersen, M., Jonasson, J. and Vehtari, A. (2019). Leave-One-Out Cross-Validation for Large Data. In International Conference on Machine Learning
Magnusson, M., Riis Andersen, M., Jonasson, J. and Vehtari, A. (2019). Leave-One-Out Cross-Validation for Model Comparison in Large Data.
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