mGNtanh
uses Gauss-Newton optimization to compute the
hyperbolic tangent (tanh) estimator for the overdispersed multinomial
regression model 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.
mGNtanh(bstart, sigma2, resstart, Y, Ypos, Xarray, xvec, tvec, jacstack, itmax = 100, print.level = 0)
NA
values) are not allowed.NA
is used to denote that no regressor is
associated with the corresponding value in the matrix. The value 0 is
used in the matrix to fill in for values that do not correspond to a
regressor.sigma2
in the call to the function.psi
function for
each observation.psi
function for each observation.sum(w) < nobs*(ncats-1)/2
(weights are too small);
32, Hessian not positive definite in the final Newton step.coefficients
, vector of coefficient parameters (same as coeffvec
value in list returned by mGNtanh); tvec
, matrix of coefficient
parameters (same as coefficients
value in list returned by mGNtanh);
formation
, inverse Hessian matrix; score
, score (or gradient
element) matrix; LLvals
, list containing weighted
(LLvals$LL
) and unweighted (LLvals$LLu
) log-likelihood values;
convflag
, TRUE/FALSE convergence flag; iters
, number of
iterations done in final Gauss-Newton stage; posdef
, TRUE if Hessian is
positive definite.Y
matrix that was supplied as input, except modified by
having done Y[!Ypos] <- 0
.Ypos
matrix that was supplied as input.jacstack
that was supplied as an input argument.Xarray
that was supplied as an input argument.For additional documentation please visit http://sekhon.berkeley.edu/robust/.