projpred (version 2.8.0)

run_cvfun: Create cvfits from cvfun

Description

A helper function that can be used to create input for cv_varsel.refmodel()'s argument cvfits by running first cv_folds() and then the reference model object's cvfun (see init_refmodel()). This is helpful if \(K\)-fold CV is run multiple times based on the same \(K\) reference model refits.

Usage

run_cvfun(object, ...)
# S3 method for default
run_cvfun(object, ...)
# S3 method for refmodel
run_cvfun(
  object,
  K = if (!inherits(object, "datafit")) 5 else 10,
  folds = NULL,
  seed = NA,
  ...
)

Value

An object that can be used as input for cv_varsel.refmodel()'s argument cvfits.

Arguments

object

An object of class refmodel (returned by get_refmodel() or init_refmodel()) or an object that can be passed to argument object of get_refmodel().

...

For run_cvfun.default(): Arguments passed to get_refmodel(). For run_cvfun.refmodel(): Currently ignored.

K

Number of folds. Must be at least 2 and not exceed the number of observations. Ignored if folds is not NULL.

folds

Either NULL for determining the CV folds automatically via cv_folds() (using argument K) or a numeric (in fact, integer) vector giving the fold index for each observation. In the latter case, argument K is ignored.

seed

Pseudorandom number generation (PRNG) seed by which the same results can be obtained again if needed. Passed to argument seed of set.seed(), but can also be NA to not call set.seed() at all. If not NA, then the PRNG state is reset (to the state before calling run_cvfun()) upon exiting run_cvfun().

Examples

if (FALSE) { # requireNamespace("rstanarm", quietly = TRUE)
# Data:
dat_gauss <- data.frame(y = df_gaussian$y, df_gaussian$x)

# The `stanreg` fit which will be used as the reference model (with small
# values for `chains` and `iter`, but only for technical reasons in this
# example; this is not recommended in general):
fit <- rstanarm::stan_glm(
  y ~ X1 + X2 + X3 + X4 + X5, family = gaussian(), data = dat_gauss,
  QR = TRUE, chains = 2, iter = 500, refresh = 0, seed = 9876
)

# Define the reference model object explicitly (not really necessary here
# because the get_refmodel() call is quite fast in this example, but in
# general, this approach is faster than defining the reference model object
# multiple times implicitly):
ref <- get_refmodel(fit)

# Run the reference model object's `cvfun` (with a small value for `K`, but
# only for the sake of speed in this example; this is not recommended in
# general):
cv_fits <- run_cvfun(ref, K = 2, seed = 184)

# Run cv_varsel() (with L1 search and small values for `nterms_max` and
# `nclusters_pred`, but only for the sake of speed in this example; this is
# not recommended in general) and use `cv_fits` there:
cvvs_L1 <- cv_varsel(ref, method = "L1", cv_method = "kfold",
                     cvfits = cv_fits, nterms_max = 3, nclusters_pred = 10,
                     seed = 5555)
# Now see, for example, `?print.vsel`, `?plot.vsel`, `?suggest_size.vsel`,
# and `?ranking` for possible post-processing functions.

# The purpose of run_cvfun() is to create an object that can be used in
# multiple cv_varsel() calls, e.g., to check the sensitivity to the search
# method (L1 or forward):
cvvs_fw <- cv_varsel(ref, method = "forward", cv_method = "kfold",
                     cvfits = cv_fits, nterms_max = 3, nclusters = 5,
                     nclusters_pred = 10, seed = 5555)

# Stratified K-fold CV is straightforward:
n_strat <- 3L
set.seed(692)
# Some example strata:
strat_fac <- sample(paste0("lvl", seq_len(n_strat)), size = nrow(dat_gauss),
                    replace = TRUE,
                    prob = diff(c(0, pnorm(seq_len(n_strat - 1L) - 0.5), 1)))
table(strat_fac)
# Use loo::kfold_split_stratified() to create the folds vector:
folds_strat <- loo::kfold_split_stratified(K = 2, x = strat_fac)
table(folds_strat, strat_fac)
# Call run_cvfun(), but this time with argument `folds` instead of `K` (here,
# specifying argument `seed` would not be necessary because of the set.seed()
# call above, but we specify it nonetheless for the sake of generality):
cv_fits_strat <- run_cvfun(ref, folds = folds_strat, seed = 391)
# Now use `cv_fits_strat` analogously to `cv_fits` from above.
}