Description Usage Arguments Value

A function for termwise feasiblity

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`formula` |
an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. |

`data` |
a data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. |

`fitfunc` |
the method that should be used to fit the model. For Example: lm, glm, or other methods that rely on formula, data, and other inputs. |

`fixvar` |
variable(s) to fix in the model. Usually a covariate that should always be included (Example: Age, Sex). Will still consider it with interactions. Default is NULL. |

`quad` |
Include quadratic terms or not. Logical. |

`cores` |
number of cores to use while running. Note: Windows can only use 1 core. See mclapply for details. If function detects a Windows user it will automatically set cores=1. |

`criterion` |
which criterion function to either maximize or minimize. For linear models one can use: r.squared, adj.r.squared, cv5.lmFSA (5 Fold Cross Validation error), cv10.lmFSA (10 Fold Cross Validation error), apress (Allen's Press Statistic), int.p.val (Interaction P-value), AIC, BIC. |

`minmax` |
whether to minimize or maximize the criterion function |

`checkfeas` |
vector of variables that could be a feasible solution. These variables will be used as the last random start. |

`var4int` |
specification of which variables to check for marginal feasiblilty. Default is NULL |

`min.nonmissing` |
the combination of predictors will be ignored unless this many of observations are not missing |

`...` |
other arguments passed to fitfunc. |

matrix of results

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