treedacv: treeda cross validation

In jfukuyama/treeDA: Tree-Based Discriminant Analysis

Description

Performs cross-validation of a treeda fit.

Usage

treedacv(
  response,
  predictors,
  tree,
  folds = 5,
  pvec = 1:tree$Nnode,
  k = nclasses - 1,
  center = TRUE,
  scale = TRUE,
  class.names = NULL,
  ...
)

Arguments

response	The classes to be predicted.
predictors	A matrix of predictors corresponding to the tips of the tree.
tree	A tree object of class phylo.
folds	Either a single number corresponding to the number of folds of cross-validation to perform or a vector of integers ranging from 1 to the number of folds desired giving the partition of the dataset.
pvec	The values of p to use.
k	The number of discriminating axes to keep.
center	Center the predictors?
scale	Scale the predictors?
class.names	A vector giving the names of the classes.
...	Additional arguments to be passed to treeda.

Value

A list with the value of p with minimum cv error (p.min), the minimum value of p with in 1 se of the minimum cv error (p.1se), and a data frame containing the loss for each fold, mean loss, and standard error of the loss for each value of p (loss.df).

Examples

data(treeda_example)
out.treedacv = treedacv(response = treeda_example$response,
    predictors = treeda_example$predictors,
    tree = treeda_example$tree,
    pvec = 1:10)
out.treedacv

jfukuyama/treeDA documentation built on May 18, 2021, 8:52 a.m.