View source: R/fastret.workflow.R

fastret.workflow | R Documentation |

Whole retention time prediction workflow. Function creates predictor set with RCDK based on SMILES. Trains a chosen predcition model and validates the approach with a cross validation.

fastret.workflow( data, method = "glmnet", verbose = FALSE, data_set_name = "data set", final_model = T, preprocessed = F, interaction_terms = F, nfolds = 2, include_polynomial = F, degree_polynomial = 2, scale = T )

`data` |
data.frame with columns NAME, RT, SMILES |

`method` |
prediction algorithm, either glmnet or xgboost |

`verbose` |
additional print outputs to user if TRUE |

`data_set_name` |
name of dataset will appear on validation plot |

`final_model` |
TRUE if final model trained on whole dataset should be returned |

`preprocessed` |
TRUE if data is already preprocessed and descriptor varialbes are already added |

`interaction_terms` |
TRUE if interaction terms between all variables should be added |

`nfolds` |
number of folds for cross validation |

`include_polynomial` |
TRUE if polynomial terms should be added to descriptor set |

`degree_polynomial` |
specifies degree up until which polynomials will be added if include_polynomials == TRUE |

`scale` |
if TRUE, all variables will be centered to a mean of 0 and scaled to a standard deviation of 1 |

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