Package flanagan.analysis

Examples of flanagan.analysis.Regression

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        // Obtain the intercept and gradient of the Gaussian probabilty plot
        ArrayMaths st4 = new ArrayMaths(this.standardizedTransformedData);
        st4 = st4.sort();
        double[] ordered = st4.array();
        Regression reg = new Regression(this.gaussianOrderMedians, ordered);
        reg.linear();
        double[] coeff = reg.getBestEstimates();
        this.transformedIntercept = coeff[0];
        this.transformedGradient = coeff[1];

        // Adust mean and standard deviation of the transformed data to match those of the entered data
        this.scaledTransformedData = Stat.scale(this.standardizedTransformedData, this.originalMean, this.originalStandardDeviation);
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        // Standardize
        st = new Stat(yTrans);
        yTransform = st.standardize();

        // Calculate and return probability plot correlation coefficient
        reg = new Regression(gaussianOrderMedians, yTransform);
        reg.linear();
        return reg.getSampleR();

    }
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        // Calculate Gaussian N[0,1] order statistic medians
        this.gaussianOrderMedians = Stat.gaussianOrderStatisticMedians(this.nData);

        // calculate the correlation coefficient of the probability plot for the untransformed data
        Regression reg = new Regression(this.gaussianOrderMedians, ((new ArrayMaths(this.standardizedOriginalData)).sort().array()));
        reg.linear();
        this.originalSampleR = reg.getSampleR();
        double[] coeff = reg.getBestEstimates();
        this.originalIntercept = coeff[0];
        this.originalGradient = coeff[1];
        coeff = reg.getBestEstimatesErrors();
        this.originalInterceptError = coeff[0];
        this.originalGradientError = coeff[1];

        this.initializationDone = true;
    }
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        // Obtain the intercept and gradient of the Gaussian probabilty plot
        ArrayMaths st4 = new ArrayMaths(this.standardizedTransformedData);
        st4 = st4.sort();
        double[] ordered = st4.array();
        Regression reg = new Regression(this.gaussianOrderMedians, ordered);
        reg.linear();
        coeff = reg.getBestEstimates();
        this.transformedIntercept = coeff[0];
        this.transformedGradient = coeff[1];
        coeff = reg.getBestEstimatesErrors();
        this.transformedInterceptError = coeff[0];
        this.transformedGradientError = coeff[1];

        // Adust mean and standard deviation of the transformed data to match those of the entered data
        this.scaledTransformedData = Stat.scale(this.standardizedTransformedData, this.originalMean, this.originalStandardDeviation);
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                     // overdetermined equations - least squares used - must be used with care
                     int n = bvec.length;
                     if(this.numberOfRows!=n)throw new IllegalArgumentException("Overdetermined equation solution - vector length is not equal to matrix column length");
                     Matrix avecT = this.transpose();
                     double[][] avec = avecT.getArrayCopy();
                     Regression reg = new Regression(avec, bvec);
                    reg.linearGeneral();
                    xvec = reg.getCoeff();
                 }
                 else{
                     throw new IllegalArgumentException("This class does not handle underdetermined equations");
                 }
             }
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        am = am.sort();

        yTransform = am.standardize();

        // Calculate and return probability plot correlation coefficient
        reg = new Regression(gaussianOrderMedians, yTransform);
        reg.linear();
        return reg.getSampleR();

    }
View Full Code Here
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        // Calculate Gaussian N[0,1] order statistic medians
        this.gaussianOrderMedians = Stat.gaussianOrderStatisticMedians(this.nData);

        // calculate the correlation coefficient of the probability plot for the untransformed data
        Regression reg = new Regression(this.gaussianOrderMedians, ((new ArrayMaths(this.standardizedOriginalData)).sort().array()));
        reg.linear();
        this.originalSampleR = reg.getSampleR();
        double[] coeff = reg.getBestEstimates();
        this.originalIntercept = coeff[0];
        this.originalGradient = coeff[1];
        coeff = reg.getBestEstimatesErrors();
        this.originalInterceptError = coeff[0];
        this.originalGradientError = coeff[1];

        this.initializationDone = true;
    }
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        // Obtain the intercept and gradient of the Gaussian probabilty plot
        Stat st4 = new Stat(this.standardizedTransformedData);
        st4 = st4.sort();
        double[] ordered = st4.array();
        Regression reg = new Regression(this.gaussianOrderMedians, ordered);
        reg.linear();
        coeff = reg.getBestEstimates();
        this.transformedIntercept = coeff[0];
        this.transformedGradient = coeff[1];
        coeff = reg.getBestEstimatesErrors();
        this.transformedInterceptError = coeff[0];
        this.transformedGradientError = coeff[1];

        // Adust mean and standard deviation of the transformed data to match those of the entered data
        this.scaledTransformedData = Stat.scale(this.standardizedTransformedData, this.originalMean, this.originalStandardDeviation);
View Full Code Here
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        // Obtain the intercept and gradient of the Gaussian probabilty plot
        Stat st4 = new Stat(this.standardizedTransformedData);
        st4 = st4.sort();
        double[] ordered = st4.array();
        Regression reg = new Regression(this.gaussianOrderMedians, ordered);
        reg.linear();
        double[] coeff = reg.getBestEstimates();
        this.transformedIntercept = coeff[0];
        this.transformedGradient = coeff[1];

        // Adust mean and standard deviation of the transformed data to match those of the entered data
        this.scaledTransformedData = Stat.scale(this.standardizedTransformedData, this.originalMean, this.originalStandardDeviation);
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                     // overdetermined equations - least squares used - must be used with care
                     int n = bvec.length;
                     if(this.numberOfRows!=n)throw new IllegalArgumentException("Overdetermined equation solution - vector length is not equal to matrix column length");
                     Matrix avecT = this.transpose();
                     double[][] avec = avecT.getArrayCopy();
                     Regression reg = new Regression(avec, bvec);
                    reg.linearGeneral();
                    xvec = reg.getCoeff();
                 }
                 else{
                     throw new IllegalArgumentException("This class does not handle underdetermined equations");
                 }
             }
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Related Classes of flanagan.analysis.Regression

  • eu.planets_project.pp.plato.model.ToolExperience
  • eu.scape_project.planning.model.measurement.ToolExperience
  • flanagan.analysis.BoxCox
  • flanagan.analysis.BoxCoxFunction
  • flanagan.math.Matrix
  • flanagan.plot.PlotGraph
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