Multinomial Multivariate-T Regression {multinomRob}R Documentation

Multinomial Multivariate-T Estimation


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 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,,

Jasjeet S. Sekhon, UC Berkeley,,


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.

For additional documentation please visit

See Also optim.

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