Description Usage Arguments Value Author(s) Examples

View source: R/multiAdaSampling.R

Performs multiple Adaptive sampling to train a classifier model

1 | ```
multiAdaSampling(dat, lab)
``` |

`data` |
A dimension reduced matrix. |

`label` |
A vector of label information for each sample. |

`seed` |
Seed before base classifier model. |

`classifier` |
Base classifier model, either "SVM" or "RF" |

`percent` |
Percentage of samples to select at each iteration during. |

`L` |
Number of ensembles. Default to 10. |

`prob` |
logical flag to return sample's probabilities to each class. |

`balance` |
logical flag to down sample large cell types classes to the median of all class sizes. |

`iter` |
A number of iterations to perform adaSampling. |

A final prediction, probabilities for each cell type and the model are returned as a list.

Pengyi Yang

1 2 3 4 5 6 7 8 | ```
data("GSE87795_liver.development.data")
mat.expr = GSE87795_liver.development.data$data
cellTypes = GSE87795_liver.development.data$cellTypes
mat.pc = matPCs(mat.expr)
result = multiAdaSampling(mat.pc, cellTypes, seed = 1, classifier = "svm", percent = 1, L = 10)
``` |

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