 | Logistic |
The LogisticRegressionParameterCalc type exposes the following members.
Methods| Name | Description | |
|---|---|---|
 | AddInterceptParameter |
Adds an intercept parameter to the model and recalculates the model parameters.
(Inherited from RegressionBase) |
| AddObservation |
Adds the given observation to the model, and recalculates the model parameters.
(Inherited from RegressionBase) |
| AddObservations |
Adds the given observations to the model, and recalculates the model parameters.
(Inherited from RegressionBase) |
| AddPredictor |
Adds a predictor to the model, and recalculates the model parameters.
(Inherited from RegressionBase) |
| AddPredictors |
Adds predictors to the model, and recalculates the model parameters.
(Inherited from RegressionBase) |
| Clone | Creates a deep copy of this LogisticRegression. |
 | DesignVariables(IDFColumn) | Convenience method for generating design, or dummy, variables which replace independent variables in a logistic model that take on discrete, nominal scaled values. The encoding method used is "reference cell coding". where the group with the SMALLEST code serves as the reference group. The method is described in the remark below. |
| DesignVariables(IDFColumn, IComparable) | Convenience method for generating design, or dummy, variables which replace independent variables in a logistic model that take on discrete, nominal scaled values. The encoding method used is "reference cell coding" with the group with the speficied code serves as the reference group. The method is described in the remark below. |
| LogLikelihood | Computes the value of the log likelihood function for a binomial logistic regression model with the given predictor variable values, probabilities and observed dichotomous outcomes. |
| ObservationVarianceEstimates | Computes the variance estimate for each observation. if pi is the probability of success predicted by the model for the ith observation, then the estimated variance for the ith observation is pi*(1.0 - pi). |
| PredictedProbabilities(DoubleMatrix) | Returns a vector of predicted probabilities of a positive outcome for the predictor variable values contained in the rows of the input matrix A. |
| PredictedProbabilities(DoubleMatrix, DoubleVector, Boolean) | Returns a vector of predicted probabilities of a positive outcome for the predictor variable values contained in the rows of the input matrix A. |
| PredictedProbability(DoubleVector) | Returns the probability of a positive outcome predicted by the model for the given set of predictor values. |
| PredictedProbability(DoubleVector, DoubleVector, Boolean) | Returns the probability of a positve outcome predicted by the model for the given set of predictor values. |
| RecalculateParameters |
Recalculates the model parameters.
(Overrides RegressionBaseRecalculateParameters) |
| RemoveInterceptParameter |
Removes the intercept parameter from the model, and recalculates the model parameters.
(Inherited from RegressionBase) |
| RemoveObservation |
Removes the row at the indicated index from the predictor matrix and the
corresponding element from the observation vector, and recalculates the model
parameters.
(Inherited from RegressionBase) |
| RemoveObservations |
Removes the specified rows from the predictor matrix, and recalculates the model
parameters.
(Inherited from RegressionBase) |
| RemovePredictor |
Removes the specified predictor from the model, and recalculates the
model parameters.
(Inherited from RegressionBase) |
 | RemovePredictors |
Removes the specified predictors from the model, and recalculates the model
parameters.
(Inherited from RegressionBase) |
| SetRegressionData | Sets the regression matrix, observation vector, and intercept option to the specified values, and recalculates the model parameters. |
See Also