Description Usage Arguments Details Author(s) References Examples
View source: R/BiomarkerDiscovery.R
This function implements the AUCRF algorithm for identifying the variables (metabolites) most relevant for the classification task
1 2 3 |
data |
a n x p dataframe used to execute the AUCRF algorithm and perform a repetead CV of the AUCRF process.
The dependent variable must be a binary variable defined as a |
seed |
a numeric value to set the seed of R's random number generator |
ref_level |
the class assumed as reference for the binary classification |
auc_rank |
the importance measure provided by |
auc_ntree |
the number of tree of each random forest model used |
auc_nfolds |
the number of folds in cross validation. By default a 5-fold cross validation is performed |
auc_pdel |
the fraction of variables to be removed at each step. If auc_pdel = 0, it will be removed only one variable at each step |
auc_colour |
the color chosen |
auc_iterations |
a numeric that represents the number of cross validation repetitions |
Exploting the AUCRF algorithm, the fuction allows to identify the best performing 'parsimonious' model in terms of OOB-AUC and the most relevant variables (metabolites) involved in the prediction task.
Piergiorgio Palla
Calle ML, Urrea V, Boulesteix A-L, Malats N (2011) 'AUC-RF: A new strategy for genomic pro- filing with Random Forest'. Human Heredity
1 2 | ## data(cachexiaData)
## aucMCV(cachexiaData, ref_level = 'control')
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