Description Usage Arguments Value Author(s) See Also

This function performs a bootstrap sampling to rank the most frequent variables that statistically aid the models by minimizing the residuals. After the frequency rank, the function uses a forward selection procedure to create a final model, whose terms all have a significant contribution to the net residual improvement (NeRI).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
ForwardSelection.Model.Res(size = 100,
fraction = 1,
pvalue = 0.05,
loops = 100,
covariates = "1",
Outcome,
variableList,
data,
maxTrainModelSize = 20,
type = c("LM", "LOGIT", "COX"),
testType=c("Binomial", "Wilcox", "tStudent", "Ftest"),
timeOutcome = "Time",
cores = 6,
randsize = 0,
featureSize=0)
``` |

`size` |
The number of candidate variables to be tested (the first |

`fraction` |
The fraction of data (sampled with replacement) to be used as train |

`pvalue` |
The maximum |

`loops` |
The number of bootstrap loops |

`covariates` |
A string of the type "1 + var1 + var2" that defines which variables will always be included in the models (as covariates) |

`Outcome` |
The name of the column in |

`variableList` |
A data frame with two columns. The first one must have the names of the candidate variables and the other one the description of such variables |

`data` |
A data frame where all variables are stored in different columns |

`maxTrainModelSize` |
Maximum number of terms that can be included in the model |

`type` |
Fit type: Logistic ("LOGIT"), linear ("LM"), or Cox proportional hazards ("COX") |

`testType` |
Type of non-parametric test to be evaluated by the |

`timeOutcome` |
The name of the column in |

`cores` |
Cores to be used for parallel processing |

`randsize` |
the model size of a random outcome. If randsize is less than zero. It will estimate the size |

`featureSize` |
The original number of features to be explored in the data frame. |

`final.model` |
An object of class |

`var.names` |
A vector with the names of the features that were included in the final model |

`formula` |
An object of class |

`ranked.var` |
An array with the ranked frequencies of the features |

`formula.list` |
A list containing objects of class |

`variableList` |
A list of variables used in the forward selection |

Jose G. Tamez-Pena and Antonio Martinez-Torteya

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