| Multinomial Regression Tanh Estimator {multinomRob} | R Documentation |
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.
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 (NA values) are not allowed. |
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,
pop <- apply(Y * ifelse(Ypos,1,0), 1, sum). |
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 M-estimator. 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 mGNtanh. |
weights |
The matrix of tanh weights for the orthogonalized residuals. The matrix
has the same dimensions as the outcome count matrix Y. The first
column of the matrix has names for the observations, and the remaining
columns contain the weights. Each of the latter columns has a name
derived from the choice.labels vector: column i+1 is named
paste("weights:",choice.labels[i],sep="").
If sum(Ypos[i,]==FALSE)>0, then values of NA appear in
weights[i,], with sum(is.na(weights[i,]))==sum(!Ypos[i,]).
The NA values will be the last values in the affected
row of the weights matrix, regardless of which outcome alternatives
were unavailable for the observation. |
Hdiag |
The matrix of weights used to fully studentize the orthogonalized
residuals. The matrix has the same dimensions as the outcome count matrix
Y. The first column of the matrix has names for the observations,
and the remaining columns contain the weights. Each of the latter columns
has a name derived from the choice.labels vector: column i+1
is named paste("Hdiag:",choice.labels[i],sep="").
If sum(Ypos[i,]==FALSE)>0, then values of 0 appear in
Hdiag[i,], with sum(is.na(Hdiag[i,]))==sum(!Ypos[i,]).
The 0 values created for this reason will be the last values in the
affected row of the Hdiag matrix, regardless of which outcome
alternatives were unavailable for the observation. |
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 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. |
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/.