CategoricalGenLinModel Properties |
The CategoricalGenLinModel type exposes the following members.
Name | Description | |
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CaseAnalysis |
The case analysis.
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CensorColumn |
The column number in x which contains the interval
type for each observation.
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ClassificationVariableColumn |
An index vector to contain the column numbers in x
that are classification variables.
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ClassificationVariableCounts |
The number of values taken by each classification variable.
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ClassificationVariableValues |
The distinct values of the classification variables in ascending
order.
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ConvergenceTolerance |
The convergence criterion.
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CovarianceMatrix |
The estimated asymptotic covariance matrix of the coefficients.
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DesignVariableMeans |
The means of the design variables.
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ExtendedLikelihoodObservations |
A vector indicating which observations are included in the extended
likelihood.
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FixedParameterColumn |
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.
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FrequencyColumn |
The column number in x that contains the frequency
of response for each observation.
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Hessian |
The Hessian computed at the initial parameter estimates.
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InfiniteEstimateMethod |
Specifies the method used for handling infinite estimates.
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LastParameterUpdates |
The last parameter updates (excluding step halvings).
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LowerEndpointColumn |
The column number in x that contains the lower
endpoint of the observation interval for full interval and right
interval observations.
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MaxIterations |
The maximum number of iterations allowed.
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ModelIntercept |
The intercept option.
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NRowsMissing |
The number of rows of data in x that contain
missing values in one or more specific columns of x.
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ObservationMax |
The maximum number of observations that can be handled in the linear
programming.
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OptimizedCriterion |
The optimized criterion.
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OptionalDistributionParameterColumn |
The column number in x that contains an optional
distribution parameter for each observation.
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Parameters |
Parameter estimates and associated statistics.
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Product |
The inverse of the Hessian times the gradient vector computed at the
input parameter estimates.
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Tolerance | The tolerance used in determining linear dependence.
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UpperBound |
Defines the upper bound on the sum of the number of distinct values
taken on by each classification variable.
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UpperEndpointColumn |
The column number in x that contains the upper
endpoint of the observation interval for full interval and left
interval observations.
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