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.
ClassificationCV(
d = NULL,
g = NULL,
n = 1,
k = 1,
rand = F,
method = c("svm", "C50", "rpart", "ropls"),
method.control = list(),
silent = FALSE
)A list of classification results which can be analyzed for accuracy, miss-classified samples and more.
Data matrix or data.frame with named rows (samples) and columns (traits).
Group-vector, factor.
Replicates of classifications.
Number of folds per replicate.
Randomize Group-vector (and apply according n and k to this randomization).
Currently svm, ropls and decision tree methods C50 and rpart are supported.
A list of parameters, forwarded to the respective classification function.
Logical. Set TRUE to suppress progress bar and warnings.
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.