Description Usage Arguments Value Author(s) References See Also Examples

`glmrobMT`

is used to fit generalized linear models by robust MT
method. The model is specified by the `x`

and `y`

components and a description of the error distribution. Currently,
only implemented for `family=poisson`

.

1 2 3 4 |

`x` |
design matrix of dimension |

`y` |
vector of observations of length |

`weights` |
an optional vector of weights to be used in the fitting process (in addition to the robustness weights computed in the fitting process). |

`start` |
starting values for the parameters in the linear predictor.
Note that specifying |

`offset` |
this can be used to specify an |

`family` |
a description of the error distribution and link function to
be used in the model. This can be a character string naming a
family function, a family |

`weights.on.x` |
a character string (can be abbreviated), a If If it is a |

`control` |
a list of parameters for controlling the fitting process.
See the documentation for |

`intercept` |
logical indicating if an intercept at the first column of |

`trace.lev` |
logical (or integer) indicating if intermediate results
should be printed; defaults to |

`include.cubinf` |
logical, if |

`m.approx` |
a function that return the value, for each linear predictor, that
makes the estimating equation Fisher consistent. If |

`mprime.approx` |
a function that return the value, for each linear predictor,
corresponding to the first derivative of |

`...` |
At the moment it is not used. |

A list with the following components:

`coefficients` |
a named vector of coefficients. |

`initial` |
Initial vector of coefficients. |

`family` |
the |

`residuals` |
weighted Pearson residuals. |

`fitted.values` |
the fitted mean values, obtained by transforming the linear predictors by the inverse of the link function. |

`linear.predictors` |
the linear fit on link scale. |

`cov` |
the estimated asymptotic covariance matrix of the estimated coefficients. |

`converged` |
logical. Was the IWLS algorithm judged to have converged? |

`iter` |
the number of iterations used by the influence algorithm. |

`cw` |
the tuning constant c in Tukey's bisquare psi-function. |

`weights.on.x` |
how the weights on the design matrix |

`w.x` |
weights used to down-weight observations based on the position of the observation in the design space. |

`w.r` |
robustness weights for each observations. |

Claudio Agostinelli, Marina Valdora and Victor J. Yohai

C. Agostinelli, M. Valdora and V.J Yohai (2018) Initial Robust Estimation in Generalized Linear Models with a Large Number of Covariates. Submitted.

M. Valdora and V.J. Yohai (2014) Robust estimators for generalized linear models. Journal of Statistical Planning and Inference, 146, 31-48.

1 2 3 4 5 6 | ```
data(epilepsy)
Efit1 <- glm(Ysum ~ Age10 + Base4*Trt, family=poisson, data=epilepsy)
x <- model.matrix( ~ Age10 + Base4*Trt, data=epilepsy)
poissonMTsetwd(tempdir())
Efit2 <- glmrobMT(x=x, y=epilepsy$Ysum)
``` |

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