Description Usage Arguments Value Author(s) References Examples
View source: R/averageExpressionPathwaySVM.R
Cross validation for aepSVM (aepSVM) using SAM to select significant differential expressed genes
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x |
a p x n matrix of expression measurements with p samples and n genes. |
y |
a factor of length p comprising the class labels. |
folds |
number of -folds cross validation (CV) |
repeats |
number of CV repeat times |
parallel |
paralle computing or not |
cores |
cores used in parallel computing |
DEBUG |
show more results or not |
Gsub |
Adjacency matrix of Protein-protein interaction network |
Cs |
soft-margin tuning parameter of the SVM. Defaults to |
seed |
seed for random sampling. |
a LIST for Cross-Validation results
auc |
The AUC values of each test fold |
fits |
The tranined models for traning folds |
feat |
The feature selected by each by the fits |
labels |
the original lables for training |
Yupeng Cun yupeng.cun@gmail.com
Guo et al., Towards precise classification of cancers based on robust gene functional expression profiles. BMC Bioinformatics 2005, 6:58.
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