StatBean Name: MultipleRegression

Purpose: Fits a multiple regression model to describe the relationship between Y and one or more numeric predictor variables.

DataSource: any.

Read/Write Properties

Name	Type	Description	Possible Values	Default Value
includeConstant	boolean	Whether to include a constant term in the model.	true,false	true
maximumSteps	int	Maximum number of steps if stepwise selection.	1+	1000
pToEnter	double	P value to enter coefficient into model.	0.000001-0.999999	0.05
pToRemove	double	P value to remove coefficient into model.	0.000001-0.999999, >= pToEnter	0.05
selectionMethod	String	Selection method for independent variables.	"All", "Forward," "Backward"	"All"
tablewiseExclusion	boolean	Whether all rows of the data table containing a missing value in any column should be excluded from the calculations.	true,false	false
tolerance	double	Conditioning tolerance for aborting matrix inversion.	>0 and <=0.000001	0.0000000001
xVariableNames	String[]	The names of the column with data values to be used for the independent (X) variables.	Any string.	""
yVariableName	String	The name of the column with data values to be used for the dependent (Y) variable.	Any string.	"Y"

Other Public Methods

Name	Description	Arguments	Return Value
double getAdjustedRSquared()	Returns the adjusted coefficient of determination.	None.	Adjusted R-squared, or missingValueCode if model cannot be fit.
void getCoefficientPValues(double[numberOfCoefficients])	Returns the P-values for the estimated coefficients.	Double output array.	None.
void getCoefficients(double[numberOfCoefficients])	Returns the estimated coefficients.	Double output array.	None.
void getCooksDistance(double c[n])	Returns Cook's distance corresponding to each row in the datasource.	Double output array.	None.
void getDegreesOfFreedom(int df[3])	Returns the degrees of freedom corresponding to the sums of squares.	Double output array.	None.
void getDFFITS(double d[n])	Returns the DFFITS statistic corresponding to each row in the datasource.	Double output array.	None.
double getDurbinWatson()	Returns the Durbin-Watson statistic.	None.	DW, or missingValueCode if model cannot be fit.
void getInModel(boolean[])	Returns true if an X variable is in the final model.	Boolean output array.	None.
int getNumberOfCoefficients()	Returns the number of coefficients in the fitted model.	None.	Number of coefficients.
void getLeverages(double h[n])	Returns the leverage corresponding to each row in the datasource.	Double output array.	None.
double getLowerConfidenceLimit(double x[],double conflevel)	Returns the lower confidence limit for the mean value of Y.	Values of X at which to make prediction, and the percentage confidence.	Lower limit.
double getLowerPredictionLimit(double x[],double meansize,double conflevel)	Returns the lower prediction limit for a new value of Y.	Values of X at which to make prediction,number of observations at X, and the percentage confidence.	Lower limit.
void getMahalanobisDistance(double c[n])	Returns the Mahalanobis distance corresponding to each row in the datasource.	Double output array.	None.
double getMeanAbsoluteError()	Returns the residual mean absolute error.	None.	MAE, or missingValueCode if model cannot be fit.
double getMeanSquaredError()	Returns the residual mean squared error.	None.	MSE, or missingValueCode if model cannot be fit.
double getModelPValue()	Returns the P-value for the fitted model.	None.	P-value.
void getPredictedValues(double p[n])	Returns the predicted value of Y corresponding to each row in the datasource.	Double output array.	None.
double getPrediction(double x[])	Returns the predicted value of Y.	Value of X's at which to make prediction.	Predicted value.
double getResidualDegreesOfFreedom()	Returns the d.f. for the error term used to estimate the standard errors.	None.	Residual df, or 0 if model cannot be fit.
void getResiduals(double r[n])	Returns the residual corresponding to each row in the datasource.	Double output array.	Residual or missingValueCode.
double getResidualStandardError()	Returns the estimated standard deviation of the residuals.	None.	Standard error of the estimate, or missingValueCode if model cannot be fit.
double getRSquared()	Returns the coefficient of determination.	None.	R-squared, or missingValueCode if model cannot be fit.
double getSampleSize()	Returns the number of non-missing data values.	None.	Sample size.
void getStandardErrors(double[numberOfCoefficients])	Returns the coefficient standard errors.	Double output array.	None.
void getStudentizedResiduals(double s[n])	Returns the studentized deleted residual corresponding to each row in the datasource.	Double output array.	None.
void getSumsOfSquares(double ss[3])	Returns the following sums of squares: total, model, residual.	Double output array.	None.
void getTypeISumsOfSquares(double[])	Returns the reduction in the residual sum of squares as each variable is entered into the model.	Double output array.	None.
double getUpperConfidenceLimit(double x[],double conflevel)	Returns the upper confidence limit for the mean value of Y.	Values of X at which to make prediction, and the percentage confidence.	Upper limit.
double getUpperPredictionLimit(double x[],double meansize,double conflevel)	Returns the upper prediction limit for a new value of Y.	Values of X at which to make prediction, number of observations at X, and the percentage confidence.	Upper limit.
void getVIF(double[])	Returns the variance inflation factors for each X variable in the final model.	Double output array.	None.
void getXTXInverse(double xtxinv[])	Returns the inverted X-transpose-X matrix.	Output array of dimension equal to number of coefficients in model squared.	Inverted matrix.

Output Variables

Name	Description
CooksD	Cook's distance corresponding to each row in the datasource.
DFFITS	The DFFITS statistic corresponding to each row in the datasource.
Leverage	The leverage corresponding to each row in the datasource.
MahalanobisD	The Mahalanobis distance corresponding to each row in the datasource.
Predicted	The predicted value of Y corresponding to each row in the datasource.
Residual	The residual corresponding to each row in the datasource.
SResidual	The studentized deleted residual corresponding to each row in the datasource.

Other properties are inherited from the general CalculationStatbean class.

Code Sample

//create a datasource bean
FileDataSource fileDataSource1 = new STATBEANS.FileDataSource();

//set the file name to be read
fileDataSource1.setFileName("c:\\statbeans\\samples\\cardata.txt");

//create a calculation bean
MultipleRegression multipleRegression1 = new STATBEANS.MultipleRegression();

//set the column names
multipleRegression1.setYVariableName("mpg");
java.lang.String[] tempString = new String[3];
tempString[0] = "weight";
tempString[1] = "horsepower";
tempString[2] = "displace";
multipleRegression1.setXVariableNames(tempString);

//create a table bean
MultipleRegressionTable multipleRegressionTable1 = new STATBEANS.MultipleRegressionTable();

//create plot beans
MultipleRegressionComponentPlot multipleRegressionPlot1 = new STATBEANS.MultipleRegressionComponentPlot();
MultipleRegression2DResponsePlot multipleRegressionPlot2 = new STATBEANS.MultipleRegression2DResponsePlot();
MultipleRegressionContourPlot multipleRegressionPlot3 = new STATBEANS.MultipleRegressionContourPlot();
MultipleRegressionSurfacePlot multipleRegressionPlot4 = new STATBEANS.MultipleRegressionSurfacePlot();

//set the column for the x axis
multipleRegression1.setXVariableName("weight");
//make the calculation bean a listener for changes in the FileDataSource bean
fileDataSource1.addDataChangeListener(multipleRegression1.listenerForDataChange);

//make the table and plot beans listeners for changes in the calculation bean
multipleRegression1.addDataChangeListener(multipleRegressionTable1.listenerForDataChange);
multipleRegression1.addDataChangeListener(multipleRegressionPlot1.listenerForDataChange);
multipleRegression1.addDataChangeListener(multipleRegressionPlot2.listenerForDataChange);
multipleRegression1.addDataChangeListener(multipleRegressionPlot3.listenerForDataChange);
multipleRegression1.addDataChangeListener(multipleRegressionPlot4.listenerForDataChange);

//set holdat values
double hold[]=new double[3];
hold[0]=2500;
hold[1]=90;
hold[2]=150;
multipleRegressionPlot2.setHoldAt(hold);
multipleRegressionPlot3.setHoldAt(hold);
multipleRegressionPlot4.setHoldAt(hold);

//instruct the fileDataSource bean to read the file
fileDataSource1.readData();