rf.kfold: k-fold cross validation for random forest

In marccamb/optiranger: Optimisation of random forest for classification on microbial community data

k-fold cross validation for random forest

Description

Splits the dataset in k and grows k random forests for classification, using alternatively each of the k parts of the dataset to make predictions, while the other k-1 parts are used for the training.

Usage

rf.kfold(
  tab,
  treat,
  k.fold = 5,
  mtry = NULL,
  n.tree = 500,
  importance_p = F,
  seed = NULL
)

Arguments

tab	An abundance table containing samples in columns and OTUs/ASV in rows.
treat	A boolean vector containing the class identity of each sample, i.e. the treatment to predict. This means that you should pick a class as a reference for the calculation of precision and sensitivity.
k.fold	A number of fold to be applied for k-fold cross-valisation.
mtry	The mtry parameter to be passed to the ranger function. See ranger documentation for details.
n.tree	The number of tree to grow in each of the k forests. The default is 500.
importance_p	A boolean defining if the p-value should be computed for the importance of variable. For now, the importance is the Gini index, and the p-value is estimated by permutation with the Altmann method. See ranger documentation for details
seed	A number to set the seed before before growing each forest. The default is NULL.

Value

A list object containing:

• a summary table with the number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) the error rate, the sensistivity TP/(TP + FN), and the precision TP/(TP + FP)

• The confusion matrix

• n.forest tables containing Gini index for each variable in each of the n.forest grown forests. This index gives the variable importance for classification.

Examples

# Coming soon!

marccamb/optiranger documentation built on June 19, 2024, 9:18 a.m.