ClassificationCV: ClassificationCV.

In MetabolomicsBasics: Basic Functions to Investigate Metabolomics Data Matrices

ClassificationCV.

Description

ClassificationCV will perform a classification using SVM's and/or Decision Trees including cross validation on a data set according to a provided grouping vector.

Usage

ClassificationCV(
  d = NULL,
  g = NULL,
  n = 1,
  k = 1,
  rand = F,
  method = c("svm", "C50", "rpart", "ropls"),
  method.control = list(),
  silent = FALSE
)

Arguments

d	Data matrix or data.frame with named rows (samples) and columns (traits).
g	Group-vector, factor.
n	Replicates of classifications.
k	Number of folds per replicate.
rand	Randomize Group-vector (and apply according n and k to this randomization).
method	Currently svm, ropls and decision tree methods C50 and rpart are supported.
method.control	A list of parameters, forwarded to the respective classification function.
silent	Logical. Set TRUE to suppress progress bar and warnings.

Details

This function allows to demonstrate the functionality of different classification tools with respect to building classifiers for metabolomics data. Check the examples in ClassificationWrapper for automatic multi-fold analysis.

Value

A list of classification results which can be analyzed for accuracy, miss-classified samples and more.

MetabolomicsBasics documentation built on Nov. 2, 2023, 5:07 p.m.