Multinomial MultivariateT Regression
Multinomial MultivariateT Estimation
multinomT
fits the multinomial multivariatet 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.
 Keywords
 models, regression
Usage
multinomT(Yp, Xarray, xvec, jacstack, start = NA, nobsvec, fixed.df = NA)
Arguments
 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 variancecovariance matrix;start$df
, the value for the multivariatet degrees of freedom parameter.  nobsvec
 Vector of the total number of counts for each observation.
 fixed.df
 The degrees of freedom to be used for the multivariatet distribution. When this is specified, the DF will not be estimated.
Details
The function often provides good starting values for multinomRob's LQD estimator, but the standard errors it reports are not correct, in part because they ignore heteroscedasticity.
Value
 call
 Names and values of all of the arguments which were passed
to the function. See
match.call
for further details.  logL
 Log likelihood.
 deviance
 Deviance.
 par
 A list of three kinds of parameter estimates:
par$beta
, the estimates for the regression coefficients;par$Omega
, the estimates for the variancecovariance matrix;par$df
, the estimate of the multivariatet 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
.
References
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): 391410. http://sekhon.berkeley.edu/multinom.pdf For additional documentation please visit http://sekhon.berkeley.edu/robust/.