A fitted Binary Generalized Extreme Value Additive object returned by function `bgeva`

and of class.

`fit` |
A list of values and diagnostics extracted from the output of the algorithm. For instance, |

`coefficients` |
The coefficients of the fitted model provided as follows. Parametric and regression spline coefficients. |

`gam.fit` |
A univariate logistic additive model object. See the documentation of |

`sp` |
Estimated smoothing parameters of the smooth components for the fitted model. |

`fp` |
If |

`iter.sp` |
Number of iterations performed for the smoothing parameter estimation step. |

`iter.if` |
Number of iterations performed in the initial step of the algorithm. |

`iter.inner` |
Number of iterations performed inside smoothing parameter estimation step. |

`tau` |
The tail parameter of the link function. |

`n` |
Sample size. |

`X` |
It returns the design matrix associated with the linear predictor. |

`Xr` |
It returns the design matrix actually used in model fitting. |

`good` |
It returns a vector indicating which observations have been discarded in the final iteration. |

`X.d2` |
Number of columns of the design matrix. This is used for internal calculations. |

`l.sp` |
Number of smooth components. |

`He` |
Penalized hessian. |

`HeSh` |
Unpenalized hessian. |

`Vb` |
Inverse of the penalized hessian. This corresponds to the Bayesian variance-covariance matrix used for ‘confidence’ interval calculations. |

`F` |
This is given by |

`t.edf` |
Total degrees of freedom of the estimated model. It is calculated as |

`bs.mgfit` |
A list of values and diagnostics extracted from |

`conv.sp` |
If |

`wor.c` |
It contains the working model quantities given by the square root of
the weight matrix times the pseudo-data vector and design matrix, |

`eta` |
The estimated linear predictor. |

`logL` |
It returns the value of the (unpenalized) log-likelihood evaluated at the (penalized or unpenalized) parameter estimates. |

Maintainer: Giampiero Marra giampiero.marra@ucl.ac.uk

`bgeva`

, `plot.bgeva`

, `summary.bgeva`

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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