Constructs a new CategoricalGenLinModel.

Determines whether the specified Object is equal to the current Object.

Allows an Object to attempt to free resources and perform other cleanup operations before the Object is reclaimed by garbage collection.

Serves as a hash function for a particular type.

Gets the Type of the current instance.

Creates a shallow copy of the current Object.

Initializes an index vector to contain the column numbers in x associated with each effect.

Sets the initial parameter estimates option.

Returns the parameter estimates and associated statistics for a CategoricalGenLinModel object.

Returns a String that represents the current Object.

The case analysis.

The column number in x which contains the interval type for each observation.

An index vector to contain the column numbers in x that are classification variables.

The number of values taken by each classification variable.

The distinct values of the classification variables in ascending order.

The convergence criterion.

The estimated asymptotic covariance matrix of the coefficients.

The means of the design variables.

A vector indicating which observations are included in the extended likelihood.

The column number in x that contains a fixed parameter for each observation that is added to the linear response prior to computing the model parameter.

The column number in x that contains the frequency of response for each observation.

The Hessian computed at the initial parameter estimates.

Specifies the method used for handling infinite estimates.

The last parameter updates (excluding step halvings).

The column number in x that contains the lower endpoint of the observation interval for full interval and right interval observations.

The maximum number of iterations allowed.

The intercept option.

The number of rows of data in x that contain missing values in one or more specific columns of x.

The maximum number of observations that can be handled in the linear programming.

The optimized criterion.

The column number in x that contains an optional distribution parameter for each observation.

Parameter estimates and associated statistics.

The inverse of the Hessian times the gradient vector computed at the input parameter estimates.

The tolerance used in determining linear dependence.

Defines the upper bound on the sum of the number of distinct values taken on by each classification variable.

The column number in x that contains the upper endpoint of the observation interval for full interval and left interval observations.