Multinomial Multivariate-T Regression {multinomRob} | R Documentation |

`multinomT`

fits the multinomial multivariate-t regression for grouped
count data. This function is not meant to be called directly by the
user. It is called by `multinomRob`

, which constructs the
various arguments.

multinomT(Yp, Xarray, xvec, jacstack, start = NA, nobsvec, fixed.df = NA)

`Yp` |
Matrix (observations by alternatives) of outcome proportions.
Values must be between 0 and 1. Missing data (`NA` values) are
not allowed. |

`Xarray` |
Array of regressors. dim(Xarray) = c(observations, parameters, alternatives). |

`xvec` |
Matrix (parameters by alternatives) that represents the model structure. It has a 1 for an estimated parameter, an integer greater than 1 for an estimated parameter constrained equal to another estimated parameter (all parameters constrained to be equal to one another have the same integer value in xvec) and a 0 otherwize. |

`jacstack` |
Array of regressors used to facilitate computing the gradient and the hessian matrix. dim(jacstack) = c(observations, unique parameters, alternatives). |

`start` |
A list of starting values of three kinds of parameters:
`start$beta` , the values for the regression coefficients; `start$Omega` , the
values for the variance-covariance matrix; `start$df` , the
value for the multivariate-t degrees of freedom parameter. |

`nobsvec` |
Vector of the total number of counts for each observation. |

`fixed.df` |
The degrees of freedom to be used for the multivariate-t distribution. When this is specified, the DF will not be estimated. |

The function often provides good starting values for multinomRob's LQD estimator, but the standard errors it reports are not correct, in part because they ignore heteroscedasticity.

`call` |
Names and values of all of the arguments which were passed
to the function. See `match.call` for further details. |

`logL` |
Log likelihood. |

`deviance` |
Deviance. |

`par` |
A list of three kinds of parameter estimates:
`par$beta` , the estimates for the regression coefficients; `par$Omega` , the
estimates for the variance-covariance matrix; `par$df` , the
estimate of the multivariate-t degrees of freedom parameter. |

`se` |
Vector of standard errors for the regression coefficients. WARNING: these are not correct in part because the model ignores heteroscedasticity. |

`optim` |
Returned by `optim` . |

`pred` |
A matrix of predicted probabilities with the same
dimentions as `Yp` . |

Walter R. Mebane, Jr., Cornell University,
wrm1@cornell.edu, http://macht.arts.cornell.edu/wrm1/

Jasjeet S. Sekhon, UC Berkeley, sekhon@berkeley.edu, http://sekhon.berkeley.edu/

Walter R. Mebane, Jr. and Jasjeet Singh Sekhon. 2004. ``Robust Estimation
and Outlier Detection for Overdispersed Multinomial Models of Count Data.''
*American Journal of Political Science* 48 (April): 391–410.
http://sekhon.berkeley.edu/multinom.pdf

For additional documentation please visit http://sekhon.berkeley.edu/robust/.