Description Usage Arguments Value Author(s) References See Also Examples

This function draws two sets of vectors (train and test samples' labels) from a binomial distribution and generates two gene expression datasets (train and test data) from a multivariate normal distribution with a mean vector U[6,10] and a given within-class covariance matrix, at each iteration.

1 | ```
generateGED(covAll, nTrain, nTest, log2FC = 1, niter = 3, prob = 0.5)
``` |

`covAll` |
an object returned by covMax or a list containing a covariance matrix cov and the proportion of DE genes pie. |

`nTrain` |
the number of samples in the training set |

`nTest` |
the number of samples in the test set |

`log2FC` |
the absolute Log2 fold changes (effect sizes) for DE genes (Default is 1) |

`niter` |
the number of iterations (train/test datasets to be generated). Default is 3 |

`prob` |
the probability of success for the binomial sampling. Default is 0.5 |

A list of length niter. Each element of which is a list containing:

`trainData` |
a matrix of the training data |

`trainLabels` |
a binary vector of class labels of the training samples |

`testData` |
a matrix of the test data |

`testLabels` |
a binary vector of class labels of the test samples |

Victor Lih Jong

Jong VL, Novianti PW, Roes KCB & Eijkemans MJC. Selecting a classification function for class prediction with gene expression data. Bioinformatics (2016) 32(12): 1814-1822

`covMat`

, `directClass`

and `plotDirectClass`

1 2 3 4 |

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