private void computeCoefficientOfDetermination() {
int numberOfItems = scatterpointsDataset.getSeries(0).getItemCount();
double arithmeticMeanOfX = 0; // arithmetic mean of X
double arithmeticMeanOfY = 0; // arithmetic mean of Y
double varX = 0; // variance of X
double varY = 0; // variance of Y
double coVarXY = 0; // covariance of X and Y;
// compute arithmetic means
for (int i = 0; i < numberOfItems; i++) {
arithmeticMeanOfX += scatterpointsDataset.getXValue(0, i);
arithmeticMeanOfY += scatterpointsDataset.getYValue(0, i);
}
arithmeticMeanOfX /= numberOfItems;
arithmeticMeanOfY /= numberOfItems;
// compute variances and covariance
for (int i = 0; i < numberOfItems; i++) {
varX += Math.pow(scatterpointsDataset.getXValue(0, i) - arithmeticMeanOfX, 2);
varY += Math.pow(scatterpointsDataset.getYValue(0, i) - arithmeticMeanOfY, 2);
coVarXY +=
(scatterpointsDataset.getXValue(0, i) - arithmeticMeanOfX)
* (scatterpointsDataset.getYValue(0, i) - arithmeticMeanOfY);
}
// computation of coefficient of determination
double r2 = Math.pow(coVarXY, 2) / (varX * varY);
r2 = MathUtils.round(r2, Math.pow(10.0, 5));
final double[] coefficients = Regression.getOLSRegression(scatterpointsDataset, 0);
final double intercept = coefficients[0];
final double slope = coefficients[1];
final String linearEquation;
if (intercept >= 0) {
linearEquation = "y = " + (float) slope + "x + " + (float) intercept;
} else {
linearEquation = "y = " + (float) slope + "x - " + Math.abs((float) intercept);
}
TextTitle tt = new TextTitle(linearEquation + "\nR² = " + r2);
tt.setTextAlignment(HorizontalAlignment.RIGHT);
tt.setFont(chart.getLegend().getItemFont());
tt.setBackgroundPaint(new Color(200, 200, 255, 100));
tt.setFrame(new BlockBorder(Color.white));
tt.setPosition(RectangleEdge.BOTTOM);
r2Annotation = new XYTitleAnnotation(0.98, 0.02, tt, RectangleAnchor.BOTTOM_RIGHT);
r2Annotation.setMaxWidth(0.48);
getPlot().addAnnotation(r2Annotation);
}
private XYIntervalSeries computeRegressionData(double xStart, double xEnd) {
if (scatterpointsDataset.getItemCount(0) > 1) {
final double[] coefficients = Regression.getOLSRegression(scatterpointsDataset, 0);
final Function2D curve = new LineFunction2D(coefficients[0], coefficients[1]);
final XYSeries regressionData =
DatasetUtilities.sampleFunction2DToSeries(curve, xStart, xEnd, 100, "regression line");
final XYIntervalSeries xyIntervalRegression = new XYIntervalSeries(regressionData.getKey());
for (int i = 0; i < regressionData.getItemCount(); i++) {
XYDataItem item = regressionData.getDataItem(i);
final double x = item.getXValue();
final double y = item.getYValue();
xyIntervalRegression.add(x, x, x, y, y, y);
}
return xyIntervalRegression;
} else {
JOptionPane.showMessageDialog(
this,
"Unable to compute regression line.\n"
+ "At least 2 values are needed to compute regression coefficients.");
return null;
}
}
public static JFreeChartWrapper regressionChart() {
DefaultTableXYDataset ds = new DefaultTableXYDataset();
XYSeries series;
series = new XYSeries("BAR", false, false);
series.add(1, 1);
series.add(2, 4);
series.add(3, 6);
series.add(4, 9);
series.add(5, 9);
series.add(6, 11);
ds.addSeries(series);
JFreeChart scatterPlot =
ChartFactory.createScatterPlot(
"Regression", "X", "Y", ds, PlotOrientation.HORIZONTAL, true, false, false);
XYPlot plot = (XYPlot) scatterPlot.getPlot();
double[] regression = Regression.getOLSRegression(ds, 0);
// regression line points
double v1 = regression[0] + regression[1] * 1;
double v2 = regression[0] + regression[1] * 6;
DefaultXYDataset ds2 = new DefaultXYDataset();
ds2.addSeries("regline", new double[][] {new double[] {1, 6}, new double[] {v1, v2}});
plot.setDataset(1, ds2);
plot.setRenderer(1, new XYLineAndShapeRenderer(true, false));
JFreeChart c = new JFreeChart(plot);
return new JFreeChartWrapper(c);
}
