Description Usage Arguments Details Value Author(s) References
multinomTanh
fits the overdispersed multinomial regression
model for grouped count data using the hyperbolic tangent (tanh)
estimator. This function is not meant to be called directly by the
user. It is called by multinomRob
, which constructs the
various arguments.
1 2  multinomTanh(Y, Ypos, X, jacstack, xvec, tvec, pop, s2,
xvar.labels, choice.labels, print.level = 0)

Y 
Matrix (observations by alternatives) of outcome counts.
Values must be nonnegative. Missing data ( 
Ypos 
Matrix indicating which elements of Y are counts to be analyzed (TRUE) and which are values to be skipped (FALSE). This allows the set of outcome alternatives to vary over observations. 
X 
Array of regressors. dim(X) = c(observations, parameters, alternatives). 
jacstack 
Array of regressors used to facilitate computing the gradient and the hessian matrix. dim(jacstack) = c(observations, unique 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. 
tvec 
Starting values for the regression coefficient parameters, as a matrix (parameters by alternatives). Parameters that are involved in equality constraints are repeated in tvec. 
pop 
Vector giving the total number of counts for each observation. In general,

s2 
Overdispersion value. In multinomRob this is the square of the LQD scale estimate. 
xvar.labels 
Vector of labels for observations. 
choice.labels 
Vector of labels for outcome alternatives. 
print.level 
Specify 0 for minimal printing (error messages only) or 2 to print details about the tanh computations. 
The tanh estimator is a redescending Mestimator. Given an estimate of the scale of the overdispersion, the tanh estimator estimates the coefficient parameters of the linear predictors of the multinomial regression model.
multinomTanh returns a list of 5 objects. The returned objects are:
mtanh 
List of tanh estimation results from function 
weights 
The matrix of tanh weights for the orthogonalized residuals. The matrix
has the same dimensions as the outcome count matrix If 
Hdiag 
The matrix of weights used to fully studentize the orthogonalized
residuals. The matrix has the same dimensions as the outcome count matrix
If 
cr 
List of predicted outcome counts, studentized residuals and standardized residuals. 
tvec 
The tanh coefficient estimates in matrix format. The matrix has one
column for each outcome alternative. The label for each row of the matrix
gives the names of the regressors to which the coefficient values in the row
apply. The regressor names in each label are separated by a forward
slash (/), and 
Walter R. Mebane, Jr., University of Michigan,
wmebane@umich.edu, http://wwwpersonal.umich.edu/~wmebane
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/.
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