public void testNaNs() {
SimpleRegression regression = new SimpleRegression();
assertTrue("intercept not NaN", Double.isNaN(regression.getIntercept()));
assertTrue("slope not NaN", Double.isNaN(regression.getSlope()));
assertTrue("slope std err not NaN", Double.isNaN(regression.getSlopeStdErr()));
assertTrue("intercept std err not NaN", Double.isNaN(regression.getInterceptStdErr()));
assertTrue("MSE not NaN", Double.isNaN(regression.getMeanSquareError()));
assertTrue("e not NaN", Double.isNaN(regression.getR()));
assertTrue("r-square not NaN", Double.isNaN(regression.getRSquare()));
assertTrue("RSS not NaN", Double.isNaN(regression.getRegressionSumSquares()));
assertTrue("SSE not NaN", Double.isNaN(regression.getSumSquaredErrors()));
assertTrue("SSTO not NaN", Double.isNaN(regression.getTotalSumSquares()));
assertTrue("predict not NaN", Double.isNaN(regression.predict(0)));
regression.addData(1, 2);
regression.addData(1, 3);
// No x variation, so these should still blow...
assertTrue("intercept not NaN", Double.isNaN(regression.getIntercept()));
assertTrue("slope not NaN", Double.isNaN(regression.getSlope()));
assertTrue("slope std err not NaN", Double.isNaN(regression.getSlopeStdErr()));
assertTrue("intercept std err not NaN", Double.isNaN(regression.getInterceptStdErr()));
assertTrue("MSE not NaN", Double.isNaN(regression.getMeanSquareError()));
assertTrue("e not NaN", Double.isNaN(regression.getR()));
assertTrue("r-square not NaN", Double.isNaN(regression.getRSquare()));
assertTrue("RSS not NaN", Double.isNaN(regression.getRegressionSumSquares()));
assertTrue("SSE not NaN", Double.isNaN(regression.getSumSquaredErrors()));
assertTrue("predict not NaN", Double.isNaN(regression.predict(0)));
// but SSTO should be OK
assertTrue("SSTO NaN", !Double.isNaN(regression.getTotalSumSquares()));
regression = new SimpleRegression();
regression.addData(1, 2);
regression.addData(3, 3);
// All should be OK except MSE, s(b0), s(b1) which need one more df
assertTrue("interceptNaN", !Double.isNaN(regression.getIntercept()));
assertTrue("slope NaN", !Double.isNaN(regression.getSlope()));
assertTrue("slope std err not NaN", Double.isNaN(regression.getSlopeStdErr()));
assertTrue("intercept std err not NaN", Double.isNaN(regression.getInterceptStdErr()));
assertTrue("MSE not NaN", Double.isNaN(regression.getMeanSquareError()));
assertTrue("r NaN", !Double.isNaN(regression.getR()));
assertTrue("r-square NaN", !Double.isNaN(regression.getRSquare()));
assertTrue("RSS NaN", !Double.isNaN(regression.getRegressionSumSquares()));
assertTrue("SSE NaN", !Double.isNaN(regression.getSumSquaredErrors()));
assertTrue("SSTO NaN", !Double.isNaN(regression.getTotalSumSquares()));
assertTrue("predict NaN", !Double.isNaN(regression.predict(0)));
regression.addData(1, 4);
// MSE, MSE, s(b0), s(b1) should all be OK now
assertTrue("MSE NaN", !Double.isNaN(regression.getMeanSquareError()));
assertTrue("slope std err NaN", !Double.isNaN(regression.getSlopeStdErr()));
assertTrue("intercept std err NaN", !Double.isNaN(regression.getInterceptStdErr()));
}
public void testCorr() {
SimpleRegression regression = new SimpleRegression();
regression.addData(corrData);
assertEquals("number of observations", 17, regression.getN());
assertEquals("r-square", .896123, regression.getRSquare(), 10E-6);
assertEquals("r", -0.94663767742, regression.getR(), 1E-10);
}
/**
* Computes the Pearson's product-moment correlation coefficient between the two arrays. Throws
* IllegalArgumentException if the arrays do not have the same length or their common length is
* less than 2
*
* @param xArray first data array
* @param yArray second data array
* @return Returns Pearson's correlation coefficient for the two arrays
* @throws IllegalArgumentException if the arrays lengths do not match or there is insufficient
* data
*/
public double correlation(final double[] xArray, final double[] yArray)
throws IllegalArgumentException {
SimpleRegression regression = new SimpleRegression();
if (xArray.length == yArray.length && xArray.length > 1) {
for (int i = 0; i < xArray.length; i++) {
regression.addData(xArray[i], yArray[i]);
}
return regression.getR();
} else {
throw MathRuntimeException.createIllegalArgumentException(
"invalid array dimensions. xArray has size {0}; yArray has {1} elements",
xArray.length, yArray.length);
}
}
public void SummuarySimulation() {
calculatePower();
PrintStream Pout1 = null;
PrintStream Pout2 = null;
PrintStream Pout3 = null;
PrintStream Pout4 = null;
PrintStream Pout5 = null;
PrintStream Pout6 = null;
ChiSquaredDistribution chi = new ChiSquaredDistributionImpl(weight.length);
try {
Pout1 = new PrintStream(new BufferedOutputStream(new FileOutputStream("LogAP.txt")));
Pout2 = new PrintStream(new BufferedOutputStream(new FileOutputStream("LNP.txt")));
Pout3 = new PrintStream(new BufferedOutputStream(new FileOutputStream("LN.txt")));
Pout4 = new PrintStream(new BufferedOutputStream(new FileOutputStream("Wald.txt")));
Pout5 = new PrintStream(new BufferedOutputStream(new FileOutputStream("F.txt")));
Pout6 = new PrintStream(new BufferedOutputStream(new FileOutputStream("LogDP.txt")));
} catch (Exception E) {
E.printStackTrace(System.err);
}
for (int i = 0; i < SimulationResults.size(); i++) {
ArrayList PointStatistics = (ArrayList) SimulationResults.get(i);
double[] LRT = new double[PointStatistics.size()];
double[] WALD = new double[PointStatistics.size()];
DescriptiveStatistics dsLRT = new DescriptiveStatisticsImpl();
DescriptiveStatistics dsWALD = new DescriptiveStatisticsImpl();
SimpleRegression sr = new SimpleRegression();
for (int j = 0; j < PointStatistics.size(); j++) {
PointMappingStatistic pms = (PointMappingStatistic) PointStatistics.get(j);
double lod = pms.get_LOD() > 0 ? pms.get_LOD() : 0;
double ln = pms.get_LN();
double p = 0;
try {
p = chi.cumulativeProbability(ln);
} catch (Exception E) {
E.printStackTrace(System.err);
}
double logLOD = -1 * Math.log10(1 - p);
Pout1.print(pms.get_logP_additive() + " ");
Pout2.print(logLOD + " ");
Pout3.print(pms.get_LN() + " ");
Pout4.print(pms.get_wald() + " ");
Pout5.print(pms.get_logP_F() + " ");
Pout6.print(pms.get_logP_dominance() + " ");
dsLRT.addValue(pms.get_LN());
dsWALD.addValue(pms.get_wald());
LRT[j] = pms.get_LN();
WALD[j] = pms.get_wald();
sr.addData(pms.get_LN(), pms.get_wald());
}
System.out.println(
dsLRT.getMean()
+ " +- "
+ dsLRT.getStandardDeviation()
+ " "
+ dsWALD.getMean()
+ " +- "
+ dsWALD.getStandardDeviation()
+ " cor "
+ sr.getR());
dsLRT.clear();
dsWALD.clear();
sr.clear();
Pout1.println();
Pout2.println();
Pout3.println();
Pout4.println();
Pout5.println();
Pout6.println();
}
Pout1.close();
Pout2.close();
Pout3.close();
Pout4.close();
Pout5.close();
Pout6.close();
}