kfold.hsstan: K-fold cross-validation
In hsstan: Hierarchical Shrinkage Stan Models for Biomarker Selection

Description Usage Arguments Value Examples

Perform K-fold cross-validation using the same settings used when fitting the model on the whole data.

## S3 method for class 'hsstan'
kfold(
  x,
  folds,
  chains = 1,
  store.fits = TRUE,
  cores = getOption("mc.cores", 1),
  ...
)

`x`	An object of class `hsstan`.
`folds`	Integer vector with one element per observation indicating the cross-validation fold in which the observation should be withdrawn.
`chains`	Number of Markov chains to run. By default this is set to 1, independently of the number of chains used for `x`.
`store.fits`	Whether the fitted models for each fold should be stored in the returned object (`TRUE` by default).
`cores`	Number of cores to use for parallelization (the value of `options("mc.cores")` by default). The cross-validation folds will be distributed to the available cores, and the Markov chains for each model will be run sequentially.
`...`	Further arguments passed to `rstan::sampling()`.

An object with classes kfold and loo that has a similar structure as the objects returned by loo() and waic() and is compatible with the loo_compare function for comparing models. The object contains the following fields:

`estimates`	a matrix containing point estimates and standard errors of the expected log pointwise predictive density ("elpd_kfold"), the effective number of parameters ("p_kfold", always `NA`) and the K-fold information criterion "kfoldic" (which is `-2 * elpd_kfold`, i.e., converted to the deviance scale).
`pointwise`	a matrix containing the pointwise contributions of "elpd_kfold", "p_kfold" and "kfoldic".
`fits`	a matrix with two columns and number of rows equal to the number of cross-validation folds. Column `fit` contains the fitted `hsstan` objects for each fold, and column `test.idx` contains the indices of the withdrawn observations for each fold. This is not present if `store.fits=FALSE`.
`data`	the dataset used in fitting the model (before withdrawing observations). This is not present if `store.fits=FALSE`.

# continued from ?hsstan
# only 2 folds for speed of example
folds <- rep(1:2, length.out=length(df$Y))
cv.biom <- kfold(hs.biom, folds=folds, cores=2)