Java tutorial
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See the GNU Lesser General Public License for more details see http://opensource.org/licenses/lgpl-license.php. http://www.SOCR.ucla.edu http://wiki.stat.ucla.edu/socr It s Online, Therefore, It Exists! ****************************************************/ /* modified annie che 200508. separate the jri part from the model part */ /* should change all the var to Java convention */ package edu.ucla.stat.SOCR.analyses.jri.gui; import edu.ucla.stat.SOCR.distributions.*; import java.awt.*; import java.awt.event.*; import javax.swing.*; import java.beans.*; import java.io.*; import java.util.ArrayList; import java.util.HashMap; import edu.ucla.stat.SOCR.analyses.data.DataType; import edu.ucla.stat.SOCR.analyses.jri.data.*; import edu.ucla.stat.SOCR.analyses.jri.result.*; import edu.ucla.stat.SOCR.analyses.exception.*; import edu.ucla.stat.SOCR.analyses.model.AnalysisType; import edu.ucla.stat.SOCR.analyses.gui.Chart; import edu.ucla.stat.SOCR.analyses.xml.XMLComposer; import edu.ucla.stat.SOCR.util.AnalysisUtility; import org.jfree.chart.JFreeChart; import org.jfree.chart.ChartPanel; /** this class is for Simple Linear Regression only. */ public class SimpleLinearRegression extends Analysis implements PropertyChangeListener { // This must be the same as what's in the HashMap pointed by Data // Otherwise you won't get anything. // Perhaps I will figure out a better way to do it. annieche. public String[][] example = new String[1][1]; // the example data public String[] columnNames = new String[1]; private double[] xData = null; private double[] yData = null; private double[] predicted = null; private double[] residuals = null; private double[] sortedResiduals = null; private double[] sortedStandardizedResiduals = null; private int[] sortedResidualsIndex = null; private double[] sortedNormalQuantiles = null; private double[] sortedStandardizedNormalQuantiles = null; private double slope, intercept; private String dependentHeader = null, independentHeader = null; static int times = 0; FileDialog fileDialog; Frame fileDialogFrame = new Frame(); File file; //FileInputStream fstream; private String fileName = ""; private boolean useHeader = true; private String header = null; //RegressionScatterPlot ScatterPlot; public JTabbedPane tabbedPanelContainer; //objects private JToolBar toolBar; private Frame frame; private JScrollPane graphPane = new JScrollPane(); /**Initialize the Analysis*/ public void init() { super.init(); analysisType = AnalysisType.SIMPLE_LINEAR_REGRESSION; ////////System.out.println("SLR analysisType = " + analysisType); useInputExample = false; useLocalExample = false; useRandomExample = true; useServerExample = false; useStaticExample = true; useGraph = true; callServer = true; onlineDescription = "http://mathworld.wolfram.com/LinearRegression.html"; depMax = 1; // max number of dependent var indMax = 1; // max number of independent var resultPanelTextArea.setFont(new Font(outputFontFace, Font.BOLD, outputFontSize)); frame = getFrame(this); setName("Regression & Correlation Analysis"); // Create the toolBar toolBar = new JToolBar(); createActionComponents(toolBar); this.getContentPane().add(toolBar, BorderLayout.NORTH); chartFactory = new Chart(); resetGraph(); validate(); // reset(); } /** Create the actions for the buttons */ protected void createActionComponents(JToolBar toolBar) { super.createActionComponents(toolBar); } /**This method sets up the analysis protocol, when the applet starts*/ public void start() { } /**This method defines the specific statistical Analysis to be carried our on the user specified data. ANOVA is done in this case. */ public void doAnalysis() { if (dataTable.isEditing()) dataTable.getCellEditor().stopCellEditing(); if (!hasExample) { JOptionPane.showMessageDialog(this, DATA_MISSING_MESSAGE); return; } if (dependentIndex < 0 || independentIndex < 0 || independentLength == 0) { JOptionPane.showMessageDialog(this, VARIABLE_MISSING_MESSAGE); return; } resultPanelTextArea.append("\nSample size=" + dataTable.getRowCount() + " \n"); dependentHeader = columnModel.getColumn(dependentIndex).getHeaderValue().toString().trim(); independentHeader = columnModel.getColumn(independentIndex).getHeaderValue().toString().trim(); resultPanelTextArea.append("\nDEPENDENT = " + dependentHeader + " \n"); resultPanelTextArea.append("\nINDEPENDENT = " + independentHeader + " \n"); Data data = new Data(); /****************************************************************** From this point, the code has been modified to work with input cells that are empty. ******************************************************************/ int xLength = 0; int yLength = 0; //resultPanelTextArea.append("\nRESULT:\n" ); String cellValue = null; ArrayList<String> xList = new ArrayList<String>(); ArrayList<String> yList = new ArrayList<String>(); try { for (int k = 0; k < dataTable.getRowCount(); k++) { try { cellValue = ((String) dataTable.getValueAt(k, dependentIndex)).trim(); if (cellValue != null && !cellValue.equals("")) { yList.add(yLength, cellValue); yLength++; } else { continue; // to the next for } } catch (Exception e) { // do nothing? } } for (int k = 0; k < dataTable.getRowCount(); k++) { try { cellValue = ((String) dataTable.getValueAt(k, independentIndex)).trim(); if (cellValue != null && !cellValue.equals("")) { xList.add(xLength, cellValue); xLength++; } else { continue; // to the next for } } catch (Exception e) { } } } catch (Exception e) { ////////System.out.println("Exception In outer catch: " + e ); } // Call the Controller method getAnalysis() delegate the work to Model xData = new double[xLength]; yData = new double[yLength]; for (int i = 0; i < yLength; i++) { yData[i] = Double.parseDouble((String) yList.get(i)); //resultPanelTextArea.append(" Y = "+yData[i] ); } for (int i = 0; i < xLength; i++) { xData[i] = Double.parseDouble((String) xList.get(i)); //resultPanelTextArea.append(" X = "+xData[i] ); } /*********** plotting data is ready here ***************/ /* where: x = data of x coordiante entered from the user */ /* y = data of y coordiante entered from the user */ data.appendX("X", xData, DataType.QUANTITATIVE); data.appendY("Y", yData, DataType.QUANTITATIVE); SimpleLinearRegressionResult result = null; boolean errorOccurs = false; String errorMessage = ""; String errorTagStart = "<error_message>"; String errorTagEnd = "</error_message>"; try { xmlInputString = data.getAnalysisXMLInputString(AnalysisType.SIMPLE_LINEAR_REGRESSION); setXMLInputString(xmlInputString); System.out.println("\n\nSimpleLinearRegression xmlInputString =" + xmlInputString); xmlOutputString = getAnalysisOutputFromServer(xmlInputString); ////System.out.println("\n\nSimpleLinearRegression PRINT FROM SERVER......... xmlOutputString =" + xmlOutputString ); if (xmlOutputString.indexOf(errorTagStart) > 0) { ////System.out.println("xmlOutputString.indexOf IF error = " + xmlOutputString.indexOf("error")); errorOccurs = true; errorMessage = xmlOutputString.substring( xmlOutputString.indexOf(errorTagStart) + errorTagStart.length(), xmlOutputString.indexOf(errorTagEnd)); } else { ////System.out.println("xmlOutputString.indexOf ELSE error = " + xmlOutputString.indexOf("error")); result = new SimpleLinearRegressionResult(xmlOutputString); } } catch (Exception e) { ////System.out.println("SimpleLinearRegression callServer Exception " + e ); } ////System.out.println("SimpleLinearRegression errorOccurs " + errorOccurs ); // Retreive the data from Data Object using HashMap if (errorOccurs) { resultPanelTextArea.append("\nTHERE IS A PROBLEM: " + errorMessage); resultPanelTextArea.setForeground(Color.BLACK); } else { double beta = 0, alpha = 0, meanX = 0, meanY = 0, sdAlpha = 0, sdBeta = 0; double tStatAlpha = 0, tStatBeta = 0; String pvAlpha = null, pvBeta = null, betaName = null; residuals = new double[xLength]; predicted = new double[xLength]; try { residuals = result.getResiduals(); for (int i = 0; i < residuals.length; i++) { //////System.out.println("result residuals["+i+"] = " + residuals[i]); } } catch (NullPointerException e) { //////System.out.println("residuals Exception " + e); } try { predicted = result.getPredicted(); for (int i = 0; i < predicted.length; i++) { //////System.out.println("result predicted["+i+"] = " + predicted[i]); } } catch (NullPointerException e) { //////System.out.println("predicted Exception " + e); } try { beta = result.getBeta(); } catch (NullPointerException e) { //showError("NullPointerException = " + e); } try { betaName = result.getBetaName(); } catch (NullPointerException e) { //showError("NullPointerException = " + e); } try { alpha = result.getAlpha(); } catch (NullPointerException e) { ////System.out.println("try alpha NullPointerException = " + e); } try { sdAlpha = result.getAlphaSE(); } catch (NullPointerException e) { //showError("\nNullPointerException = " + e); } try { sdBeta = result.getBetaSE(); } catch (NullPointerException e) { //showError("\nNullPointerException = " + e); } try { tStatAlpha = result.getAlphaTStat(); } catch (NullPointerException e) { //showError("\nNullPointerException = " + e); } try { tStatBeta = result.getBetaTStat(); } catch (NullPointerException e) { //showError("\nNullPointerException = " + e); } try { pvAlpha = result.getAlphaPValue(); } catch (NullPointerException e) { //showError("\nNullPointerException = " + e); } try { pvBeta = result.getBetaPValue(); } catch (NullPointerException e) { //showError("\nNullPointerException = " + e); } try { sortedResiduals = result.getSortedResiduals();//(double[]) } catch (NullPointerException e) { //showError("\nNullPointerException = " + e); } try { sortedStandardizedResiduals = result.getSortedStandardizedResiduals(); } catch (NullPointerException e) { //showError("\nNullPointerException = " + e); } try { sortedResidualsIndex = result.getSortedResidualsIndex(); } catch (NullPointerException e) { //showError("\nNullPointerException = " + e); } try { sortedNormalQuantiles = result.getSortedNormalQuantiles(); } catch (NullPointerException e) { //showError("\nNullPointerException = " + e); } try { sortedStandardizedNormalQuantiles = result.getSortedStandardizedNormalQuantiles(); } catch (NullPointerException e) { //showError("\nNullPointerException = " + e); } resultPanelTextArea.append("\nNUMBER OF OBSERVATIONS = " + xLength); resultPanelTextArea.append("\n INTERCEPT SLOPE"); resultPanelTextArea.append("\nESTIMATES " + alpha + " " + beta); resultPanelTextArea.append("\nSTANDARD ERRORS " + sdAlpha + " " + sdBeta); resultPanelTextArea .append("\nT-STATISTICS " + tStatAlpha + " " + tStatBeta); resultPanelTextArea .append("\nP-VALUE " + pvAlpha + " " + pvBeta); /* resultPanelTextArea.append("\nSE of ALPHA = " + sdAlpha); resultPanelTextArea.append("\nSE of BETA = " + sdBeta); resultPanelTextArea.append("\nT-STAT of ALPHA = " + tStatAlpha); resultPanelTextArea.append("\nT_STAT of BETA = " + tStatBeta); resultPanelTextArea.append("\nP-VALUE of ALPHA = " + pvAlpha); resultPanelTextArea.append("\nP-VALUE of BETA = " + pvBeta); */ resultPanelTextArea.append("\n\nPREDICTED VALUES RESIDUALS"); for (int i = 0; i < xLength; i++) { try { resultPanelTextArea.append("\n" + predicted[i] + " " + residuals[i]); } catch (Exception e) { } } /* resultPanelTextArea.append("\nRESIDUALS: " ); for (int i = 0; i < xLength; i++) { try { resultPanelTextArea.append(" " + residuals[i]); } catch (Exception e) { } } /* resultPanelTextArea.append("\nRESIDUALS SORTED= " ); for (int i = 0; i < xLength; i++) { resultPanelTextArea.append(" " + sortedResiduals[i]); } resultPanelTextArea.append("\nRESIDUALS INDEX SORTED= " ); for (int i = 0; i < xLength; i++) { resultPanelTextArea.append(" " + sortedResidualsIndex[i]); } resultPanelTextArea.append("\nSORTED RESIDUALS NORMAL QUANTILES = " ); for (int i = 0; i < xLength; i++) { resultPanelTextArea.append(" " + sortedNormalQuantiles[i]); } */ resultPanelTextArea.setForeground(Color.BLUE); slope = beta; intercept = alpha; doGraph(); } } /** convert a generic string s to a fixed length one. */ public String monoString(String s) { String sAdd = new String(s + " "); return sAdd.substring(0, 14); } /** convert a generic double s to a "nice" fixed length string */ public String monoString(double s) { final double zero = 0.00001; Double sD = new Double(s); String sAdd = new String(); if (s > zero) sAdd = new String(sD.toString()); else sAdd = "<0.00001"; sAdd = sAdd.toLowerCase(); int i = sAdd.indexOf('e'); if (i > 0) sAdd = sAdd.substring(0, 4) + "E" + sAdd.substring(i + 1, sAdd.length()); else if (sAdd.length() > 10) sAdd = sAdd.substring(0, 10); sAdd = sAdd + " "; return sAdd.substring(0, 14); } /** convert a generic integer s to a fixed length string */ public String monoString(int s) { Integer sD = new Integer(s); String sAdd = new String(sD.toString()); sAdd = sAdd + " "; return sAdd.substring(0, 14); } /** Implementation of PropertyChageListener.*/ public void propertyChange(PropertyChangeEvent e) { String propertyName = e.getPropertyName(); System.err.println("From RegCorrAnal:: propertyName =" + propertyName + "!!!"); if (propertyName.equals("DataUpdate")) { //update the local version of the dataTable by outside source dataTable = (JTable) (e.getNewValue()); dataPanel.removeAll(); dataPanel.add(new JScrollPane(dataTable)); System.err.println("From RegCorrAnal:: data UPDATED!!!"); } } public Container getDisplayPane() { this.getContentPane().add(toolBar, BorderLayout.NORTH); return this.getContentPane(); } protected void doGraph() { // graphComponent is available here // data: variables double xData, yData, residuals, predicted are available here after doAnalysis() is run. //////System.out.println("start doGraph"); graphPanel.removeAll(); // 1. scatter plot of data: yData vs. xData //JFreeChart scatterChart = chartFactory.getLineChart("Scatter Plot Y vs X", independentHeader, dependentHeader, xData, yData);//getChart(title, xlabel, ylabel, xdata,ydata) JFreeChart scatterChart = chartFactory.getQQChart("Scatter Plot Y vs X", independentHeader, dependentHeader, "", xData, yData, "", intercept, slope, ""); ChartPanel chartPanel = new ChartPanel(scatterChart, false); chartPanel.setPreferredSize(new Dimension(plotWidth, plotHeight)); graphPanel.add(chartPanel); scatterChart = chartFactory.getQQChart("Scatter Plot X vs Y", dependentHeader, independentHeader, "", yData, xData, "", 0, 0, ""); chartPanel = new ChartPanel(scatterChart, false); chartPanel.setPreferredSize(new Dimension(plotWidth, plotHeight)); graphPanel.add(chartPanel); // this is only a test for having more than one charts in a boxlayout // 2. residual on fit plot: residuals vs. predicted JFreeChart rfChart2 = chartFactory.getQQChart("Residual on Fit Plot", "Predicted", "Residuals", "", predicted, residuals, "", 0, 0, ""); ChartPanel chartPanel2 = new ChartPanel(rfChart2, false); chartPanel2.setPreferredSize(new Dimension(plotWidth, plotHeight)); graphPanel.add(chartPanel2); /* // 3. residual on fit plot: residuals vs. xData JFreeChart rxChart = chartFactory.getQQChart("Residual on Covariate Plot", "Covariate", "Residuals", "", xData, residuals, "", 0, 0, ""); chartPanel = new ChartPanel(rxChart, false); chartPanel.setPreferredSize(new Dimension(plotWidth,plotHeight)); graphPanel.add(chartPanel); // 4. Normal QQ plot: need residuals and standardized normal scores JFreeChart qqChart = chartFactory.getQQChart("Residual Normal QQ Plot", "Theoretical Quantiles", "Standardized Residuals", "", sortedNormalQuantiles, sortedStandardizedResiduals, "", 0, 0, ""); chartPanel = new ChartPanel(qqChart, false); chartPanel.setPreferredSize(new Dimension(plotWidth,plotHeight)); graphPanel.add(chartPanel); // 5. scale-location plot -- maybe later. */ graphPanel.validate(); } protected void resetGraph() { //////System.out.println("reset graph in SLR"); JFreeChart chart = chartFactory.createChart(); // an empty chart ChartPanel chartPanel = new ChartPanel(chart, false); chartPanel.setPreferredSize(new Dimension(400, 300)); graphPanel.removeAll(); graphPanel.add(chartPanel); } public String getOnlineDescription() { return new String("http://en.wikipedia.org/wiki/Linear_regression"); } }