Example usage for org.apache.commons.math.stat.correlation Covariance Covariance

Introduction

In this page you can find the example usage for org.apache.commons.math.stat.correlation Covariance Covariance.

Prototype

public Covariance(RealMatrix matrix) 

Source Link

Document

Create a covariance matrix from a matrix whose columns represent covariates.

Usage

From source file:guineu.modules.dataanalysis.correlations.CorrelationTask.java

@Override
public void run() {
    setStatus(TaskStatus.PROCESSING);/*w w  w  .j  av a  2 s. c o  m*/
    try {
        Dataset data1 = dataset[0];
        Dataset data2 = null;
        if (dataset.length == 1) {
            data2 = dataset[0];
        } else if (dataset.length > 1) {
            data2 = dataset[1];
        }
        Dataset newDataset = new SimpleBasicDataset("Correlation matrix");
        newDataset.addColumnName("ID");

        // Column names from the first dataset
        for (PeakListRow row : data1.getRows()) {
            String name = row.getID() + " - " + row.getName();
            newDataset.addColumnName(name);
            if (this.showPvalue) {
                newDataset.addColumnName(name + " - pValue");
            }
        }

        // Row names from the second dataset
        for (int i = 0; i < data2.getNumberRows(); i++) {
            PeakListRow row = data2.getRow(i);
            PeakListRow newRow = new SimplePeakListRowOther();
            newRow.setPeak("ID", row.getID() + " - " + row.getName());
            newDataset.addRow(newRow);
        }

        for (PeakListRow row : data1.getRows()) {
            String name = row.getID() + " - " + row.getName();
            String pValueName = name + " - pValue";
            for (int i = 0; i < data2.getNumberRows(); i++) {
                RealMatrix matrix = getCorrelation(row, data2.getRow(i), data1.getAllColumnNames());
                PearsonsCorrelation correlation = null;
                if (this.correlationType.contains("Pearsons")) {
                    correlation = new PearsonsCorrelation(matrix);
                } else if (this.correlationType.contains("Spearmans")) {
                    SpearmansCorrelation scorrelation = new SpearmansCorrelation(matrix);
                    correlation = scorrelation.getRankCorrelation();
                }
                if (correlation != null) {
                    double c = correlation.getCorrelationMatrix().getEntry(1, 0);
                    double p = correlation.getCorrelationPValues().getEntry(1, 0);
                    String cp = String.valueOf(c);

                    Color cell = Color.WHITE;
                    if (p < cutoff) {
                        if (c < 0) {
                            cell = Color.green;
                        } else {
                            cell = Color.red;
                        }

                    }
                    int pColumnIndex = 0;
                    if (this.showPvalue) {
                        newDataset.getRow(i).setPeak(pValueName, String.valueOf(p));
                        pColumnIndex = newDataset.getAllColumnNames().indexOf(pValueName) + 2;
                        newDataset.setCellColor(cell, i, pColumnIndex);
                    }

                    pColumnIndex = newDataset.getAllColumnNames().indexOf(name) + 2;
                    newDataset.getRow(i).setPeak(name, cp);

                    newDataset.setCellColor(cell, i, pColumnIndex);

                }

                if (this.correlationType.contains("Covariance")) {
                    Covariance covariance = new Covariance(matrix);
                    double c = covariance.getCovarianceMatrix().getEntry(1, 0);
                    newDataset.getRow(i).setPeak(name, String.valueOf(c));
                }
            }
        }

        GUIUtils.showNewTable(newDataset, true);
        setStatus(TaskStatus.FINISHED);
    } catch (Exception ex) {
        Logger.getLogger(CorrelationTask.class.getName()).log(Level.SEVERE, null, ex);
        setStatus(TaskStatus.ERROR);
    }
}

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