`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.

- 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`

.

`match.call`

.
`optim`

.