com.pervasive.datarush.analytics.regression

Class AbstractPMMLRegressionModel.RegressionTable

java.lang.Object

com.pervasive.datarush.analytics.regression.AbstractPMMLRegressionModel.RegressionTable

Enclosing class:

AbstractPMMLRegressionModel

protected static final class AbstractPMMLRegressionModel.RegressionTable
extends Object

Constructor Summary

Constructors

Modifier	Constructor and Description
protected	AbstractPMMLRegressionModel.RegressionTable()

Method Summary

Methods

Modifier and Type	Method and Description
void	addCategoricalPredictor(AbstractPMMLRegressionModel.CategoricalPredictor categoricalPredictor) Add a term to the model in the form of a CategoricalPredictor element.
void	addNumericPredictor(AbstractPMMLRegressionModel.NumericPredictor numericPredictor) Add a term to the model in the form of a NumericPredictor element.
protected void	buildModelElement(Element parent) Add this RegressionTable to a PMML document as a child of the provided element.
void	clearCategoricalPredictors() Removes all the CategoricalPredictor elements from the table
void	clearNumericPredictors() Removes all the NumericPredictor elements from the table
List<AbstractPMMLRegressionModel.CategoricalPredictor>	getCategoricalPredictors() Gets the table's predictors that take a categorical (i.e.
double	getIntercept() Gets the constant term (a.k.a the intercept or bias) of the equation specified by the RegressionTable.
Integer	getInterceptDegreesOfFreedom() Gets the intercept's degrees of freedom
Double	getInterceptPValue() Gets the intercept's t test
Double	getInterceptStandardError() Gets the intercept's standard error
Double	getInterceptTTest() Gets the intercept's t test
List<AbstractPMMLRegressionModel.NumericPredictor>	getNumericPredictors() Gets the table's predictors that take a numeric value as input.
String	getTargetCategory()
protected void	parseModelElement(Element element)
void	setExtensionStatistic(String name, double value)
void	setIntercept(double value) Sets the intercept i.e.
void	setInterceptDegreesOfFreedom(int value) Sets the intercept's degrees of freedom
void	setInterceptPValue(double value) Sets the intercept's p value
void	setInterceptStandardError(double value) Sets the intercept's standard error
void	setInterceptTTest(double value) Sets the intercept's t test
void	setTargetCategory(String targetCategory)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

AbstractPMMLRegressionModel.RegressionTable

protected AbstractPMMLRegressionModel.RegressionTable()

Method Detail

parseModelElement

protected void parseModelElement(Element element)

buildModelElement

protected void buildModelElement(Element parent)

Add this RegressionTable to a PMML document as a child of the provided element.

Parameters:

parent - The parent of the table's PMML element.

setIntercept

public void setIntercept(double value)

Sets the intercept i.e. the bias term

Parameters:

value - the desired intercept

setInterceptDegreesOfFreedom

public void setInterceptDegreesOfFreedom(int value)

Sets the intercept's degrees of freedom

setInterceptStandardError

public void setInterceptStandardError(double value)

Sets the intercept's standard error

setInterceptTTest

public void setInterceptTTest(double value)

Sets the intercept's t test

setInterceptPValue

public void setInterceptPValue(double value)

Sets the intercept's p value

getNumericPredictors

public List<AbstractPMMLRegressionModel.NumericPredictor> getNumericPredictors()

Gets the table's predictors that take a numeric value as input. The returned list does not support modification.

Returns:

the NumericPredictor elements of this RegressionTable

getCategoricalPredictors

public List<AbstractPMMLRegressionModel.CategoricalPredictor> getCategoricalPredictors()

Gets the table's predictors that take a categorical (i.e. enum) value as input. The returned list does not support modification.

Returns:

the CategoricalPredictor elements of this RegressionTable

getIntercept

public double getIntercept()

Gets the constant term (a.k.a the intercept or bias) of the equation specified by the RegressionTable.

Returns:

the value of the intercept

getInterceptDegreesOfFreedom

public Integer getInterceptDegreesOfFreedom()

Gets the intercept's degrees of freedom

getInterceptStandardError

public Double getInterceptStandardError()

Gets the intercept's standard error

getInterceptTTest

public Double getInterceptTTest()

Gets the intercept's t test

getInterceptPValue

public Double getInterceptPValue()

Gets the intercept's t test

setTargetCategory

public void setTargetCategory(String targetCategory)

Parameters:

targetCategory - the targetCategory to set

getTargetCategory

public String getTargetCategory()

Returns:

the targetCategory

addNumericPredictor

public void addNumericPredictor(AbstractPMMLRegressionModel.NumericPredictor numericPredictor)

Add a term to the model in the form of a NumericPredictor element.

Parameters:

numericPredictor - the predictor to add.

addCategoricalPredictor

public void addCategoricalPredictor(AbstractPMMLRegressionModel.CategoricalPredictor categoricalPredictor)

Add a term to the model in the form of a CategoricalPredictor element.

Parameters:

categoricalPredictor - the predictor to add.

clearNumericPredictors

public void clearNumericPredictors()

Removes all the NumericPredictor elements from the table

clearCategoricalPredictors

public void clearCategoricalPredictors()

Removes all the CategoricalPredictor elements from the table

setExtensionStatistic

public void setExtensionStatistic(String name,
                         double value)