| Variables
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Click the
Variables button to display a standard variable selection dialog box. Select one dependent variable and two or more independent variables. The independent variables can be categorical or continuous, or a combination of both.
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| Estimation Method
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In this group box, select a method of estimation for the algorithm specified in the Algorithm list on the
Specifications - Quick tab. The available choices vary based on the Algorithm selection on the
Specifications - Quick tab.
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| Linear Regression
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These methods fit the partial residual to the standardized predictors using the simple least‐ squares approach. The two methods differ in terms of complexity associated with the estimation of loss gradient at each coordinate descent step.
- Covariance update: The complexity of coordinate descent stepwise update is proportional to the number of non‐zero terms in the model. This method is more efficient than the Naive estimation.
- Naive: The complexity of coordinate descent stepwise update is proportional to the number of predictor variables.
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| Logistic Regression
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The following methods fit the partial residuals to the standardized predictors using the iteratively reweighted least squares approach. The two methods differ in terms of hessian computation at each coordinate descent step.
- Newton: This method uses the exact hessian.
- Modified Newton: This method uses a bounded hessian. This method can be more efficient.
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| Include intercept
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Select this check box to include the intercept in the model.
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| Max. vars in largest model
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Specify the maximum number of variables to be included in the model.
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| Max. iterations
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Specify the maximum number of iterations for the coordinate descent. This will be the maximum number of times data is accessed.
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| Convergence threshold
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Specify the convergence threshold for coordinate descent. This value is used to determine whether the iterative estimation procedure has converged; specifically, the integer value entered into this field is used as the (negative) exponent of a base 10 constant. For instance, if the default value 7 is used, the constant will evaluate to 10E‐7. This constant is then used to check for convergence of the iterative estimation procedure by comparing it to the absolute value of the difference of the deviance function between two successive iterations.
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| Penalties
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Click this button to display the
Set penalties for predictors dialog box. Here you can specify separate penalties to be applied to each coefficient. A value of 0 can be used to always include the variable in model.
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| Options / C / W
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For more information on
Options/C/W, see "Common Options" in
Statistica Electronic Manual.
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| OK
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Click the
OK button to accept all the specifications made in the dialog box and to close it. The analysis results are placed in the Reporting Documents node after running or updating the project.
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| Cancel
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Click the
Cancel button to close the
Lasso Regression dialog box without making any changes to the current specifications.
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