Perform exact K-fold cross-validation by refitting the model \(K\) times each leaving out one-\(K\)th of the original data. Folds can be run in parallel using the future package.
# S3 method for brmsfit
kfold(
x,
...,
K = 10,
Ksub = NULL,
folds = NULL,
group = NULL,
joint = FALSE,
compare = TRUE,
resp = NULL,
model_names = NULL,
save_fits = FALSE,
recompile = NULL,
future_args = list()
)A brmsfit object.
Further arguments passed to brm.
The number of subsets of equal (if possible) size
into which the data will be partitioned for performing
\(K\)-fold cross-validation. The model is refit K times, each time
leaving out one of the K subsets. If K is equal to the total
number of observations in the data then \(K\)-fold cross-validation is
equivalent to exact leave-one-out cross-validation.
Optional number of subsets (of those subsets defined by K)
to be evaluated. If NULL (the default), \(K\)-fold cross-validation
will be performed on all subsets. If Ksub is a single integer,
Ksub subsets (out of all K) subsets will be randomly chosen.
If Ksub consists of multiple integers or a one-dimensional array
(created via as.array) potentially of length one, the corresponding
subsets will be used. This argument is primarily useful, if evaluation of
all subsets is infeasible for some reason.
Determines how the subsets are being constructed.
Possible values are NULL (the default), "stratified",
"grouped", or "loo". May also be a vector of length
equal to the number of observations in the data. Alters the way
group is handled. More information is provided in the 'Details'
section.
Optional name of a grouping variable or factor in the model.
What exactly is done with this variable depends on argument folds.
More information is provided in the 'Details' section.
Indicates which observations' log likelihoods shall be
considered jointly in the ELPD computation. If "obs" or FALSE
(the default), each observation is considered separately. This enables
comparability of kfold with loo. If "fold", the joint
log likelihoods per fold are used. If "group", the joint log
likelihoods per group within folds are used (only available if argument
group is specified).
A flag indicating if the information criteria
of the models should be compared to each other
via loo_compare.
Optional names of response variables. If specified, predictions are performed only for the specified response variables.
If NULL (the default) will use model names
derived from deparsing the call. Otherwise will use the passed
values as model names.
If TRUE, a component fits is added to
the returned object to store the cross-validated brmsfit
objects and the indices of the omitted observations for each fold.
Defaults to FALSE.
Logical, indicating whether the Stan model should be
recompiled. This may be necessary if you are running reloo on
another machine than the one used to fit the model.
A list of further arguments passed to
future for additional control over parallel
execution if activated.
kfold returns an object that has a similar structure as the
objects returned by the loo and waic methods and
can be used with the same post-processing functions.
The kfold function performs exact \(K\)-fold
cross-validation. First the data are partitioned into \(K\) folds
(i.e. subsets) of equal (or as close to equal as possible) size by default.
Then the model is refit \(K\) times, each time leaving out one of the
K subsets. If \(K\) is equal to the total number of observations
in the data then \(K\)-fold cross-validation is equivalent to exact
leave-one-out cross-validation (to which loo is an efficient
approximation). The compare_ic function is also compatible with
the objects returned by kfold.
The subsets can be constructed in multiple different ways:
If both folds and group are NULL, the subsets
are randomly chosen so that they have equal (or as close to equal as
possible) size.
If folds is NULL but group is specified, the
data is split up into subsets, each time omitting all observations of one
of the factor levels, while ignoring argument K.
If folds = "stratified" the subsets are stratified after
group using loo::kfold_split_stratified.
If folds = "grouped" the subsets are split by
group using loo::kfold_split_grouped.
If folds = "loo" exact leave-one-out cross-validation
will be performed and K will be ignored. Further, if group
is specified, all observations corresponding to the factor level of the
currently predicted single value are omitted. Thus, in this case, the
predicted values are only a subset of the omitted ones.
If folds is a numeric vector, it must contain one element per
observation in the data. Each element of the vector is an integer in
1:K indicating to which of the K folds the corresponding
observation belongs. There are some convenience functions available in
the loo package that create integer vectors to use for this purpose
(see the Examples section below and also the
kfold-helpers page).
When running kfold on a brmsfit created with the
cmdstanr backend in a different R session, several recompilations
will be triggered because by default, cmdstanr writes the model
executable to a temporary directory. To avoid that, set option
"cmdstanr_write_stan_file_dir" to a nontemporary path of your choice
before creating the original brmsfit (see section 'Examples' below).
if (FALSE) {
fit1 <- brm(count ~ zAge + zBase * Trt + (1|patient) + (1|obs),
data = epilepsy, family = poisson())
# throws warning about some pareto k estimates being too high
(loo1 <- loo(fit1))
# perform 10-fold cross validation
(kfold1 <- kfold(fit1, chains = 1))
# use joint likelihoods per fold for ELPD evaluation
kfold(fit1, chains = 1, joint = "fold")
# use the future package for parallelization of models
# that is to fit models belonging to different folds in parallel
library(future)
plan(multisession, workers = 4)
kfold(fit1, chains = 1)
plan(sequential)
## to avoid recompilations when running kfold() on a 'cmdstanr'-backend fit
## in a fresh R session, set option 'cmdstanr_write_stan_file_dir' before
## creating the initial 'brmsfit'
## CAUTION: the following code creates some files in the current working
## directory: two 'model_<hash>.stan' files, one 'model_<hash>(.exe)'
## executable, and one 'fit_cmdstanr_<some_number>.rds' file
set.seed(7)
fname <- paste0("fit_cmdstanr_", sample.int(.Machine$integer.max, 1))
options(cmdstanr_write_stan_file_dir = getwd())
fit_cmdstanr <- brm(rate ~ conc + state, data = Puromycin,
backend = "cmdstanr", file = fname)
# now restart the R session and run the following (after attaching 'brms')
set.seed(7)
fname <- paste0("fit_cmdstanr_", sample.int(.Machine$integer.max, 1))
fit_cmdstanr <- brm(rate ~ conc + state,
data = Puromycin,
backend = "cmdstanr",
file = fname)
kfold_cmdstanr <- kfold(fit_cmdstanr, K = 2)
}