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/.