fwdglm | R Documentation |

This function applies the forward search approach to robust analysis in generalized linear models.

fwdglm(formula, family, data, weights, na.action, contrasts = NULL, bsb = NULL, balanced = TRUE, maxit = 50, epsilon = 1e-06, nsamp = 100, trace = TRUE)

`formula` |
a symbolic description of the model to be fit. The details of the model are the same as for glm. |

`family` |
a description of the error distribution and link function to be used in the model. See |

`data` |
an optional data frame containing the variables in the model. By default the variables are taken from the environment from which the function is called. |

`weights` |
an optional vector of weights to be used in the fitting process. |

`na.action` |
a function which indicates what should happen when the data contain |

`contrasts` |
an optional list. See the |

`bsb` |
an optional vector specifying a starting subset of observations to be used in the forward search. By default the |

`balanced` |
logical, for a binary response if |

`maxit` |
integer giving the maximal number of IWLS iterations. See |

`epsilon` |
positive convergence tolerance epsilon. See |

`nsamp` |
the initial subset for the forward search in generalized linear models is found by the function |

`trace` |
logical, if |

The function returns an object of class `"fwdglm"`

with the following components:

`call` |
the matched call. |

`Residuals` |
a |

`Unit` |
a matrix of units added (to a maximum of 5 units) at each step. |

`included` |
a list with each element containing a vector of units included at each step of the forward search. |

`Coefficients` |
a |

`tStatistics` |
a |

`Leverage` |
a |

`MaxRes` |
a |

`MinDelRes` |
a |

`ScoreTest` |
a |

`Likelihood` |
a |

`CookDist` |
a |

`ModCookDist` |
a |

`Weights` |
a |

`inibsb` |
a vector giving the best starting subset chosen by |

`binary.response` |
logical, equal to |

Originally written for S-Plus by:
Kjell Konis kkonis@insightful.com and Marco Riani mriani@unipr.it

Ported to R by Luca Scrucca luca@stat.unipg.it

Atkinson, A.C. and Riani, M. (2000), *Robust Diagnostic Regression Analysis*, First Edition. New York: Springer, Chapter 6.

`summary.fwdglm`

, `plot.fwdglm`

, `fwdlm`

, `fwdsco`

.

data(cellular) cellular$TNF <- as.factor(cellular$TNF) cellular$IFN <- as.factor(cellular$IFN) mod <- fwdglm(y ~ TNF + IFN, data=cellular, family=poisson(log), nsamp=200) summary(mod) ## Not run: plot(mod) plot(mod, 1) plot(mod, 5) plot(mod, 6, ylim=c(-3, 20)) plot(mod, 7) plot(mod, 8)

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