Description Usage Arguments Value References See Also Examples

Produces an object of class `glmRob`

which is a Robust Generalized Linear Model fit.

1 2 3 4 |

`formula` |
a formula expression as for other regression models, of the form response ~ predictors. See the documentation of |

`family` |
a family object - only |

`data` |
an optional data frame in which to interpret the variables occuring in the formula. |

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

`subset` |
an expression specifying the subset of the data to which the model is fit. This can be a logical vector (which is replicated to have length equal to the number of observations), a numeric vector indicating which observations are included, or a character vector of the row names to be included. By default all observations are used. |

`na.action` |
a function to filter missing data. This is applied to the |

`method` |
a character vector indicating the fitting method. The choices are |

`model` |
a logical flag. If |

`x` |
a logical flag. If |

`y` |
a logical flag. If |

`contrasts` |
a list of contrasts to be used for some or all of the factors appearing as variables in the model formula. The names of the list should be the names of the corresponding variables, and the elements should either be contrast-type matrices (matrices with as many rows as levels of the factor and with columns linearly independent of each other and of a column of one's), or else they should be functions that compute such contrast matrices. |

`control` |
a list of iteration and algorithmic constants to control the conditionally unbiased bounded influence robust fit. See |

`...` |
control arguments maybe specified directly. |

a list with class `glmRob`

containing the robust generalized linear model fit. See `glmRob.object`

for details.

Copas, J. B. (1988). Binary Regression Models for Contaminated Data. JRSS 50, 225-265.

Kunsch, L., Stefanski L. and Carroll, R. (1989). Conditionally Unbiased Bounded-Influence Estimation in General Regression Models, with Applications to Generalized Linear Models. JASA 50, 460-466.

Carroll, R. J. and Pederson, S. (1993). On Robustness in the Logistic Regression Model. JRSS 55, 693-706.

Marazzi, A. (1993). Algorithms, routines and S functions for robust statistics. Wadsworth & Brooks/Cole, Pacific Grove, CA.

`glmRob.control`

,
`glmRob.object`

,
`glmRob.cubif.control`

,
`glmRob.mallows.control`

,
`glmRob.misclass.control`

,
`glm`

.

1 2 3 4 | ```
data(breslow.dat)
glmRob(sumY ~ Age10 + Base4*Trt, family = poisson(),
data = breslow.dat, method = "cubif")
``` |

```
Loading required package: fit.models
Call:
glmRob(formula = sumY ~ Age10 + Base4 * Trt, family = poisson(),
data = breslow.dat, method = "cubif")
Coefficients:
(Intercept) Age10 Base4 Trtprogabide
1.83516 0.12081 0.13915 -0.39279
Base4:Trtprogabide
0.02182
Degrees of Freedom: 59 Total; 54 Residual
Residual Deviance: 3889
```

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