Examples of TDistributionImpl

Examples of org.apache.commons.math.distribution.TDistributionImpl

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    /**
     * Default constructor.
     */
    public TTestImpl() {
        this(new TDistributionImpl(1.0));
    }
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Examples of org.apache.commons.math.distribution.TDistributionImpl

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                if (n == 1) continue;

                double s = Math.sqrt(1.0 * m2 / (n - 1));

                double td = new TDistributionImpl(n - 1).cumulativeProbability((1 + conf.desiredConfidenceLevel) / 2);
                double confidenceRange = 2 * s / Math.sqrt(n) * td;
//                Trace.writeLine("[" + ep + "] confidence range = " + confidenceRange / 1000000 + " ms");
                if (i >= conf.minCalibrationIters && confidenceRange < conf.desiredConfidenceRangeMs * 1000000) {
//                    Trace.writeLine("[" + ep + "] Achieved desired confidence range");
                    break;
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Examples of org.apache.commons.math.distribution.TDistributionImpl

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    /**
     * Default constructor.
     */
    public TTestImpl() {
        this(new TDistributionImpl(1.0));
    }
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Examples of org.apache.commons.math.distribution.TDistributionImpl

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    /**
     * Create an empty SimpleRegression instance
     */
    public SimpleRegression() {
        this(new TDistributionImpl(1.0));
    }
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Examples of org.apache.commons.math.distribution.TDistributionImpl

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public class MathStatUtils extends TTestImpl{
 
  private TDistribution distribution;

  public MathStatUtils(){
    distribution = new TDistributionImpl(1);
  } 
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Examples of org.apache.commons.math.distribution.TDistributionImpl

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    final double regressionSumOfSquares = totalSumOfSquares - errorSumOfSquares;
    final double[][] covarianceBetas = convertArray(_algebra.inverse(_algebra.mult(transpose, matrix)).toArray());
    final double rSquared = regressionSumOfSquares / totalSumOfSquares;
    final double adjustedRSquared = 1. - (1 - rSquared) * (n - 1) / (n - k);
    final double meanSquareError = errorSumOfSquares / (n - k);
    final ContinuousDistribution studentT = new TDistributionImpl(n - k);
    for (int i = 0; i < k; i++) {
      standardErrorsOfBeta[i] = Math.sqrt(meanSquareError * covarianceBetas[i][i]);
      tStats[i] = betas[i] / standardErrorsOfBeta[i];
      try {
        pValues[i] = 1 - studentT.cumulativeProbability(Math.abs(tStats[i]));
      } catch (final org.apache.commons.math.MathException e) {
        throw new com.opengamma.analytics.math.MathException(e);
      }
    }
    return new WeightedLeastSquaresRegressionResult(betas, residuals, meanSquareError, standardErrorsOfBeta, rSquared, adjustedRSquared, tStats, pValues,
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Examples of org.apache.commons.math.distribution.TDistributionImpl

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    final double regressionSumOfSquares = totalSumOfSquares - errorSumOfSquares;
    final double[][] covarianceBetas = convertArray(_algebra.inverse(_algebra.mult(transpose, matrix)).toArray());
    final double rSquared = regressionSumOfSquares / totalSumOfSquares;
    final double adjustedRSquared = 1. - (1 - rSquared) * (n - 1.) / (n - k);
    final double meanSquareError = errorSumOfSquares / (n - k);
    final ContinuousDistribution studentT = new TDistributionImpl(n - k);
    // final ProbabilityDistribution<Double> studentT = new
    // StudentTDistribution(n - k);
    for (int i = 0; i < k; i++) {
      stdErrorBetas[i] = Math.sqrt(meanSquareError * covarianceBetas[i][i]);
      tStats[i] = betas[i] / stdErrorBetas[i];
      try {
        pValues[i] = 1 - studentT.cumulativeProbability(Math.abs(tStats[i]));
      } catch (final org.apache.commons.math.MathException e) {
        throw new com.opengamma.analytics.math.MathException(e);
      }
    }
    return new LeastSquaresRegressionResult(betas, residuals, meanSquareError, stdErrorBetas, rSquared, adjustedRSquared, tStats, pValues, useIntercept);
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