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k-fold cross-validation for pigauto trait imputation

Source: R/cross_validate.R
cross_validate.Rd

Performs stratified k-fold cross-validation by rotating which cells serve as the test set. Returns per-fold and aggregated metrics.

Usage

cross_validate(
  data,
  tree,
  k = 5L,
  seeds = NULL,
  epochs = 500L,
  verbose = TRUE,
  ...
)

Arguments

data

pigauto_data object (output of preprocess_traits).

tree

phylo object.

k

integer. Number of folds (default 5).

seeds

optional integer vector. Seeds for replicate runs. The default NULL performs one run using the current RNG stream.

epochs

integer. Training epochs per fold (default 500).

verbose

logical. Print progress (default TRUE).

...

Additional arguments passed to fit_pigauto (e.g. hidden_dim, k_eigen, use_attention).

Value

A list of class "pigauto_cv" with:

results

Data.frame with columns: fold, rep, trait, type, metric, value.

summary

Data.frame with mean and sd across folds/reps for each trait + metric.

conformal_coverage

Data.frame of coverage per trait across folds (if available).

k

Number of folds.

n_reps

Number of replicates.

Examples

# \donttest{
data(avonet300, tree300, package = "pigauto")
tree <- ape::keep.tip(tree300, tree300$tip.label[seq_len(30L)])
traits <- avonet300[match(tree$tip.label, avonet300$Species_Key),
                     c("Mass", "Wing.Length"), drop = FALSE]
rownames(traits) <- tree$tip.label
cv <- cross_validate(preprocess_traits(traits, tree), tree, k = 3L,
                     seeds = 1L, epochs = 5L, verbose = FALSE)
#> Error: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.
print(cv)
#> Error: object 'cv' not found
summary(cv)
#> Error: object 'cv' not found
# }