Description Usage Arguments Value

Computes the coefficient estimates for logistic regression. ridge regularization and bridge regularization optional. This function is to be used with the "logisticc" function.

1 2 3 | ```
CV_logisticc(X, y, lam = 0L, alpha = 0L, penalty = "none",
intercept = TRUE, method = "IRLS", tol = 1e-05, maxit = 10000,
vec = 0L, init = 0L, criteria = "logloss", K = 5L)
``` |

`X` |
matrix |

`y` |
matrix or vector of response values 0,1 |

`lam` |
vector of tuning parameters for ridge regularization term. Defaults to 'lam = 0' |

`alpha` |
vector of tuning parameters for bridge regularization term. Defaults to 'alpha = 1.5' |

`penalty` |
choose from c('none', 'ridge', 'bridge'). Defaults to 'none' |

`intercept` |
Defaults to TRUE |

`method` |
optimization algorithm. Choose from 'IRLS' or 'MM'. Defaults to 'IRLS' |

`tol` |
tolerance - used to determine algorithm convergence. Defaults to 1e-5 |

`maxit` |
maximum iterations. Defaults to 1e5 |

`vec` |
optional vector to specify which coefficients will be penalized |

`init` |
optional initialization for MM algorithm |

`criteria` |
specify the criteria for cross validation. Choose from c("mse", "logloss", "misclass"). Defauls to "logloss" |

`K` |
specify number of folds in cross validation, if necessary |

returns best lambda, best alpha, and cross validation errors

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