



import statement/s: 
For all regressions import flanagan.analysis.Regression; In addition for relevant nonlinear regressions import flanagan.analysis.RegressionFunction; import flanagan.analysis.RegressionFunction2; import flanagan.analysis.RegressionFunction3; 
Constructors  public Regression(double[ ][ ] xdata, double[ ] ydata, double[ ] yErrors)  
public Regression(double[ ][ ] xdata, double[ ] ydata, double[ ][] xerrors, double[ ] yerrors)  
public Regression(double[ ][ ] xdata, double[ ][] ydata, double[ ][ ] yerrors)  
public Regression(double[ ][ ] xdata, double[ ][] ydata, double[ ][ ] xerrors, double[ ][ ] yerrors)  
public Regression(double[ ] xdata, double[ ] ydata, double[ ] yErrors)  
public Regression(double[ ] xdata, double[ ] ydata, double[ ] xerrors, double[ ] yerrors)  
public Regression(double[ ] xdata, double[ ][] ydata, double[ ][ ] yerrors)  
public Regression(double[ ] xdata, double[ ][] ydata, double[ ] xerrors, double[ ][ ] yerrors)  
public Regression(double[ ][ ] xdata, double[ ] ydata)  
public Regression(double[ ][ ] xdata, double[ ][] ydata)  
public Regression(double[ ] xdata, double[ ] ydata)  
public Regression(double[ ] xdata, double[ ][] ydata)  
public Regression(double[ ] xdata, double binWidth, double binZero)  
public Regression(double[ ] xdata, double binWidth)  
Linear Regression 
Fitting to a constant y_{i} = a_{0}  public void constant() 
public void constantPlot() public void constantPlot(String xLegend, String yLegend)  
Linear with intercept y_{i} = a_{0}+a_{1}.x_{0,}_{i}+a_{2}.x_{1,}_{i}+... 
public void linear() public void linear(double fixedIntercept)  
public void linearPlot() public void linearPlot(double fixedIntercept) public void linearPlot(String xLegend, String yLegend) public void linearPlot(double fixedIntercept, String xLegend, String yLegend)  
General linear y_{i} = a_{0}.f_{1}(x_{0},x_{1}..)+a_{1}.f_{2}(x_{0},x_{1}+...)  public void linearGeneral()  
public void linearGeneralPlot() public void linearGeneralPlot(String xLegend, String yLegend)  
Polynomial y_{i} = a_{0}+a_{1}.x+a_{2}.x^{2}+a_{3}x^{3} ... 
public void polynomial(int n) public void polynomial(int n, double fixedIntercept)  
public void polynomialPlot(int n) public void polynomialPlot(int n, double fixedIntercept) public void polynomialPlot(int n, String xLegend, String yLegend) public void polynomialPlot(int n, double fixedIntercept, String xLegend, String yLegend)  
public ArrayList<Object> bestPolynomial() public ArrayList<Object> bestPolynomial(double fixedIntercept)  
public ArrayList<Object> bestPolynomialPlot() public ArrayList<Object> bestPolynomialPlot(double fixedIntercept) public ArrayList<Object> bestPolynomialPlot(String xLegend, String yLegend) public ArrayList<Object> bestPolynomialPlot(double fixedIntercept, String xLegend, String yLegend)  
public void setFtestSignificance(double signif) public double getFtestSignificance()  
public void nonIntegerPolynomial(int nTerms) [nonlinear regression]  
public void nonIntegerPolynomialPlot(int nTerms) [nonlinear regression] public void nonIntegerPolynomialPlot(int nTerms, String xLegend, String yLegend)  
Return the best estimates  public double[] getBestEstimates()  
Return the errors of the best estimates  public double[] getBestEstimatesErrors()  
See Common methods for list of methods associated with performing a linear regression, e.g. returning a statistical analysis of a linear regression, plotting the best fit curve.  
Nonlinear Regression WARNING 
Nelder and Mead Simplex One set of dependent variables y = f(a_{0},a_{1},a_{2}..., x_{0},x_{1},x_{2}...) Several sets of the dependent variable y_{0} = f_{1}(a_{0},a_{1},a_{2}..., x_{0},x_{1},x_{2}...) y_{1} = f_{2}(a_{0},a_{1},a_{2}..., x_{0},x_{1},x_{2}...) . . . y_{n} = f_{n}(a_{0},a_{1},a_{2}..., x_{0},x_{1},x_{2}...) 
public void simplex(RegressionFunction rf, double[ ] start, double[ ] step, double ftol, int nmax) public void simplex(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step, double ftol, int nmax) public void simplex(RegressionFunction2 rf, double[ ] start, double[ ] step, double ftol, int nmax) public void simplex(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step, double ftol, int nmax) public void simplex(RegressionFunction3 rf, double[ ] start, double[ ] step, double ftol, int nmax) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step, double ftol, int nmax) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step, double ftol, int nmax) 
public void simplexPlot(RegressionFunction rf, double[ ] start, double[ ] step, double ftol, int nmax) public void simplexPlot(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step, double ftol, int nmax) public void simplexPlot(RegressionFunction2 rf, double[ ] start, double[ ] step, double ftol, int nmax) public void simplexPlot(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step, double ftol, int nmax) public void simplexPlot(RegressionFunction3 rf, double[ ] start, double[ ] step, double ftol, int nmax) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step, double ftol, int nmax) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step, double ftol, int nmax)  
public void simplex(RegressionFunction rf, double[ ] start, double[ ] step, double ftol) public void simplex(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step, double ftol) public void simplex(RegressionFunction2 rf, double[ ] start, double[ ] step, double ftol) public void simplex(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step, double ftol) public void simplex(RegressionFunction3 rf, double[ ] start, double[ ] step, double ftol) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step, double ftol) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step, double ftol)  
public void simplexPlot(RegressionFunction rf, double[ ] start, double[ ] step, double ftol) public void simplexPlot(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step, double ftol) public void simplexPlot(RegressionFunction2 rf, double[ ] start, double[ ] step, double ftol) public void simplexPlot(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step, double ftol) public void simplexPlot(RegressionFunction3 rf, double[ ] start, double[ ] step, double ftol) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step, double ftol) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step, double ftol)  
public void simplex(RegressionFunction rf, double[ ] start, double[ ] step, int nmax) public void simplex(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step, int nmax) public void simplex(RegressionFunction2 rf, double[ ] start, double[ ] step, int nmax) public void simplex(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step, int nmax) public void simplex(RegressionFunction3 rf, double[ ] start, double[ ] step, int nmax) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step, int nmax) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step, int nmax)  
public void simplexPlot(RegressionFunction rf, double[ ] start, double[ ] step, int nmax) public void simplexPlot(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step, int nmax) public void simplexPlot(RegressionFunction2 rf, double[ ] start, double[ ] step, int nmax) public void simplexPlot(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step, int nmax) public void simplexPlot(RegressionFunction3 rf, double[ ] start, double[ ] step, int nmax) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step, int nmax) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step, int nmax)  
public void simplex(RegressionFunction rf, double[ ] start, double ftol, int nmax) public void simplex(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start, double ftol, int nmax) public void simplex(RegressionFunction2 rf, double[ ] start, double ftol, int nmax) public void simplex(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double ftol, int nmax) public void simplex(RegressionFunction3 rf, double[ ] start, double ftol, int nmax) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start, double ftol, int nmax) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double ftol, int nmax)  
public void simplexPlot(RegressionFunction rf, double[ ] start, double ftol, int nmax) public void simplexPlot(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start, double ftol, int nmax) public void simplexPlot(RegressionFunction2 rf, double[ ] start, double ftol, int nmax) public void simplexPlot(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double ftol, int nmax) public void simplexPlot(RegressionFunction3 rf, double[ ] start, double ftol, int nmax) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start, double ftol, int nmax) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double ftol, int nmax)  
public void simplex(RegressionFunction rf, double[ ] start, double[ ] step) public void simplex(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step) public void simplex(RegressionFunction2 rf, double[ ] start, double[ ] step) public void simplex(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step) public void simplex(RegressionFunction3 rf, double[ ] start, double[ ] step) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step)  
public void simplexPlot(RegressionFunction rf, double[ ] start, double[ ] step) public void simplexPlot(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step) public void simplexPlot(RegressionFunction2 rf, double[ ] start, double[ ] step) public void simplexPlot(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step) public void simplexPlot(RegressionFunction3 rf, double[ ] start, double[ ] step) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start, double[ ] step) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double[ ] step)  
public void simplex(RegressionFunction rf, double[ ] start, double ftol) public void simplex(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start, double ftol) public void simplex(RegressionFunction2 rf, double[ ] start, double ftol) public void simplex(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double ftol) public void simplex(RegressionFunction3 rf, double[ ] start, double ftol) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start, double ftol) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double ftol)  
public void simplexPlot(RegressionFunction rf, double[ ] start, double ftol) public void simplexPlot(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start, double ftol) public void simplexPlot(RegressionFunction2 rf, double[ ] start, double ftol) public void simplexPlot(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double ftol) public void simplexPlot(RegressionFunction3 rf, double[ ] start, double ftol) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start, double ftol) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start, double ftol)  
public void simplex(RegressionFunction rf, double[ ] start, int nmax) public void simplex(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start, int nmax) public void simplex(RegressionFunction2 rf, double[ ] start, int nmax) public void simplex(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start, int nmax) public void simplex(RegressionFunction3 rf, double[ ] start, int nmax) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start, int nmax) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start, int nmax)  
public void simplexPlot(RegressionFunction rf, double[ ] start, int nmax) public void simplexPlot(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start, int nmax) public void simplexPlot(RegressionFunction2 rf, double[ ] start, int nmax) public void simplexPlot(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start, int nmax) public void simplexPlot(RegressionFunction3 rf, double[ ] start, int nmax) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start, int nmax) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start, int nmax)  
public void simplex(RegressionFunction rf, double[ ] start) public void simplex(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start) public void simplex(RegressionFunction2 rf, double[ ] start) public void simplex(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start) public void simplex(RegressionFunction3 rf, double[ ] start) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start) public void simplex(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start)  
public void simplexPlot(RegressionFunction rf, double[ ] start) public void simplexPlot(RegressionFunction rf, RegressionDerivativeFunction rdf, double[ ] start) public void simplexPlot(RegressionFunction2 rf, double[ ] start) public void simplexPlot(RegressionFunction2 rf, RegressionDerivativeFunction2 rdf, double[ ] start) public void simplexPlot(RegressionFunction3 rf, double[ ] start) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction rdf, double[ ] start) public void simplexPlot(RegressionFunction3 rf, RegressionDerivativeFunction2 rdf, double[ ] start)  
Deprecated simplex methods  simplex2 and simplexPlot2  
Constrained nonlinear regression  public void addConstraint(int pIndex, int direction, double boundary)  
public void addConstraint(int pIndices, double plusOrMinus, int direction, double boundary)  
public void addConstraint(int pIndices, int plusOrMinus, int direction, double boundary)  
public void removeConstraints()  
public void setPenaltyWeight(double pWeight)  
public double getPenaltyWeight()  
public void setConstraintTolerance(double tolerance)  
Scaling the initial estimates  public void setScale(int opt)  
public void setScale(double[] opt)  
public double[ ] getScale()  
Returning the best estimates  public double[] getBestEstimates()  
Returning the errors of the best estimates  public double[] getBestEstimatesErrors()  
Returning the initial estimates  public double[ ] getInitialEstimates()  
public void[ ] getScaledInitialEstimates()  
Returning the initial step sizes  public double[ ] getInitialSteps()  
public void[ ] getScaledInitialSteps()  
Convergence tests  public void setMinTest(int opt)  
public int getMinTest()  
public void setTolerance(double tol)  
public int getTolerance()  
public double getSimplexSd()  
Restarts  public void setNrestartsMax(int nrm)  
public int getNrestartsMax()  
public int getNrestarts()  
Number of iterations  public void setNmax(int nMax)  
public int getNmax()  
public void setNmin(int nMin)  
public int getNmin()  
public int getNiter()  
Pseudolinear statistics  public void setDelta(double delta)  
public int getDelta()  
public boolean getInversionCheck()  
public boolean getPosVarCheck()  
Gradients about the minimum  public double[][] getGrad()  
Nelder and Mead Simplex Coefficients  public void setNMreflect(double reflectC)  
public double getNMreflect()  
public void setNMextend(double extendC)  
public double getNMextend()  
public void setNMcontract(double contractC)  
public double getNMcontract()  
See Common methods for list of methods associated with performing a nonlinear regression, e.g. returning a statistical analysis of a nonlinear regression, plotting the best fit curve.  
Fitting data to special Functions  Scaling of the ordinate values 
public void setYscaleFactor(double scaleFactor) 
public void setYscaleOption(boolean test)  
public boolean getYscaleOption()  
Fit to a Gaussian distribution (parameter fixing option available) or to Gaussian distributions [normal distribution]  public void gaussian()  
public void gaussian(double[] parameterValues, boolean[] fixedOptions)  
public void gaussianPlot()  
public void gaussianPlot(double[] parameterValues, boolean[] fixedOptions)  
public void multipleGaussiansPlot(int nGaussians, double[] guessesOfMeans, double[] guessesOfStandardDeviations, double[] guessesOfFractionalContributions)  
public static void fitOneOrSeveralDistributions(double[] array)  
See also the ProbabiltyPlot class methods under Gaussian Probabilty Plot  
Fit to a Lognormal distribution
(two parameter statistic) 
public void logNormal() public void logNormalTwoPar()  
public void logNormalPlot() public void logNormalTwoParPlot()  
public static void fitOneOrSeveralDistributions(double[] array)  
Fit to a Lognormal distribution
(three parameter statistic)  public void logNormalThreePar()  
public void logNormalThreeParPlot()  
public static void fitOneOrSeveralDistributions(double[] array)  
Fit to a Logistic distribution  public void logistic()  
public void logisticPlot()  
public static void fitOneOrSeveralDistributions(double[] array)  
See also the ProbabiltyPlot class methods under Logistic Probabilty Plot  
Fit to a Beta distribution  public void beta()  
public void betaPlot()  
public void betaMinMax()  
public void betaMinMaxPlot()  
public static void fitOneOrSeveralDistributions(double[] array)  
Fit to a Gamma distribution  public void gamma()  
public void gammPlot()  
public void gammaStandard()  
public void gammaStandardPlot()  
public static void fitOneOrSeveralDistributions(double[] array)  
Fit to an Erlang distribution  public void erlang()  
public void erlangPlot()  
public static void fitOneOrSeveralDistributions(double[] array)  
Fit to a Poisson distribution  public void poisson()  
public void poissonPlot()  
Fit to a Lorentzian distribution  public void lorentz()  
public void lorentzPlot()  
public static void fitOneOrSeveralDistributions(double[] array)  
Fit to a Type 1 Extreme Value Distribution (minimum order statistic) Gumbel Distribution (minimum order statistic)  public void gumbelMin()  
public void gumbelMinPlot()  
public void gumbelMinOnePar()  
public void gumbelMinOneParPlot()  
public void gumbelMinStandard()  
public void gumbelMinStandardPlot()  
public static void fitOneOrSeveralDistributions(double[] array)  
See also the ProbabiltyPlot class methods under Gumbel (minimum order statistic) Probabilty Plot  
Fit to a Type 1 Extreme Value Distribution (maximum order statistic) Gumbel Distribution (maximum order statistic)  public void gumbelMax()  
public void gumbelMaxPlot()  
public void gumbelMaxOnePar()  
public void gumbelMaxOneParPlot()  
public void gumbelMaxStandard()  
public void gumbelMaxStandardPlot()  
public static void fitOneOrSeveralDistributions(double[] array)  
See also the ProbabiltyPlot class methods under Gumbel (maxiimum order statistic) Probabilty Plot  
Fit to a Type 2 Extreme Value Distribution Fréchet distribution  public void frechet()  
public void frechetPlot()  
public void frechetTwoPar()  
public void frechetTwoParPlot()  
public void frechetStandard()  
public void frechetStandardPlot()  
public static void fitOneOrSeveralDistributions(double[] array)  
See also the ProbabiltyPlot class methods under Fréchet Probabilty Plot  
Fit to a Type 3 Extreme Value Distribution Weibull distribution  public void weibull()  
public void weibullPlot()  
public void weibullTwoPar()  
public void weibullTwoParPlot()  
public void weibullStandard()  
public void weibullStandardPlot()  
public static void fitOneOrSeveralDistributions(double[] array)  
See also the ProbabiltyPlot class methods under Weibull Probabilty Plot  
Fit to a Type 3 Extreme Value Distribution (see also fitting simple exponentials) Exponential Distribution  public void exponential()  
public void exponentialPlot()  
public void exponentialOnePar()  
public void exponentialOneParPlot()  
public void exponentialStandard()  
public void exponentialStandardPlot()  
public static void fitOneOrSeveralDistributions(double[] array)  
See also the ProbabiltyPlot class methods under Exponential Probabilty Plot  
Fit to a Type 3 Extreme Value Distribution Rayleigh Distribution  public void rayleigh()  
public void rayleighPlot()  
public static void fitOneOrSeveralDistributions(double[] array)  
See also the ProbabiltyPlot class methods under Rayleigh Probabilty Plot  
Pareto Distribution  public void paretoShifted()  
public void paretoShiftedPlot()  
public void paretoTwoPar()  
public void paretoTwoParPlot()  
public void paretoOnePar()  
public void paretoOneParPlot()  
public static void fitOneOrSeveralDistributions(double[] array)  
See also the ProbabiltyPlot class methods under Pareto Probabilty Plot  
Fit to one or several of the above distributions  public static void fitOneOrSeveralDistributions(double[] array)  
Fitting Simple Exponentials (See also fit to an Exponential Distribution) y_{i} = a.exp(b.x_{i}) y_{i} = Σa_{j}.exp(b_{j}.x_{i}) y_{i} = a.(1  exp(b.x_{i}))  public void exponentialSimple()  
public void exponentialSimplePlot()  
public void exponentialMultiple(int nExp)  
public void exponentialMultiplePlot(int nExp)  
public void exponentialMultiple(int nExp, double[] initialEstimates)  
public void exponentialMultiplePlot(int nExp, double[] initialEstimates)  
public void oneMinusExponential()  
public void oneMinusExponentialPlot()  
Fit to a Sigmoid Function Sigmoidal Threshold Function  public void sigmoidThreshold()  
public void sigmoidThesholdPlot()  
Fit to a Sigmoid Function Hill/Sips Sigmoid  public void sigmoidHillSips()  
public void sigmoidHillSipsPlot()  
Fit to a Sigmoid Function Five parameter logistic curve [5PL]  public void fiveParameterLogistic()  
public void fiveParameterLogisticPlot()  
public void fiveParameterLogistic(double bottom, double top)  
public void fiveParameterLogisticPlot(double bottom, double top)  
Fit to a Sigmoid Function Four parameter logistic curve [4PL, EC_{50} dose response curve]  public void fourParameterLogistic()  
public void ec50()  
public void fourParameterLogisticConstrained()  
public void ec50constrained()  
public void fourParameterLogisticPlot()  
public void ec50Plot()  
public void fourParameterLogisticConstrainedPlot()  
public void ec50constrainedPlot()  
public void fourParameterLogistic(double bottom, double top)  
public void ec50(double bottom, double top)  
public void fourParameterLogisticPlot(double bottom, double top)  
public void ec50Plot(double bottom, double top)  
Fit to a Rectangular Hyperbola  public void rectangularHyperbola()  
public void rectangularHyperbolaPlot()  
public void shiftedRectangularHyperbola()  
public void shiftedRectangularHyperbolaPlot()  
Fit to a Scaled Heaviside Step Function  public void stepFunction()  
public void stepFunctionPlot()  
Common instance methods
(see below for common static methods)  Input data option  public void setErrorsAsScaled(); 
public void setErrorsAsSD();  
public void setTrueFreq(boolean trueOrfalse)  
public boolean getTrueFreq()  
Add a title to graphs and output files  public void setTitle(String title)  
Print the regression results  public void print( String filename, int prec)  
public void print( String filename)  
public void print(int prec)  
public void print()  
Suppress the printing of the regression results  public void suppressPrint()  
Plot the regression results x against y  public int plotXY( String graphName)  
public int plotXY()  
public int plotXY(RegressionFunction rf, String graphName)  
public int plotXY(RegressionFunction2 rf, String graphName)  
public int plotXY(RegressionFunction3 rf, String graphName)  
public int plotXY(RegressionFunction rf)  
public int plotXY(RegressionFunction2 rf)  
public int plotXY(RegressionFunction3 rf)  
Plot the regression results y(exp) against y(calc)  public int plotYY(String filename)  
public int plotYY()  
Suppress the plot of the regression results y(exp) against y(calc)  public void suppressYYplot()  
Add axis legends to the x  y plot  public void setXlegend(String xLegend)  
public void setYlegend(String yLegend)  
Return the best estimates  public double[] getBestEstimates()  
Return the errors of the best estimates  public double[] getBestEstimatesErrors()  
Return the coefficients of variation  public double[] getCoeffVar()  
Return the tvalues of the best estimates  public double[] getTvalues()  
Return the Pvalues of the best estimates  public double[] getPvalues()  
Return the calculated y values  public double[] getYcalc()  
Return the weights  public double[] getWeights()  
Return the unweighted residuals  public double[] getResiduals()  
Return the weighted residuals  public double[] getWeightedResiduals()  
Return the unweighted sum of residual squares 
public double getSumOfSquares() public double getSumOfUnweightedResidualSquares()  
Return the weighted sum of residual squares 
public double getChiSquare() public double getSumOfWeightedResidualSquares()  
Return the reduced chi square  public double getReducedChiSquare()  
Return the total sum of weighted squares  public double getTotalSumOfWeightedSquares()  
Return the regression sum of weighted squares  public double getRegressionSumOfWeightedSquares()  
Return the linear correlation coefficient 
public double getXYcorrCoeff() public double getYYcorrCoeff()  
Return the coefficient of determination 
public double getCoefficientOfDetermination()  
Return the adjusted coefficient of determination 
public double getAdustedCoefficientOfDetermination()  
Return the coefficient of determination Fratio 
public double getCoeffDeterminationFratio()  
Return the coefficient of determination Fratio probability 
public double getCoeffDeterminationFratioProb()  
Return the degrees of freedom  public int getDegFree()  
Covariance Matrix  public double[ ][ ] getCovMatrix()  
Parameter correlation coefficients  public doublet[ ][ ] getCorrCoeffMatrix()  
Return the DurbinWatson d Statistic  public double getDurbinWatsonD()  
Check normality of residuals  public void checkResidualNormality()  
public void checkWeightedResidualNormality()  
Common static methods 
Test of additional terms (Extra sum of squares)  public static ArrayList<Object> testOfAdditionalTerms(double chiSquare1, int nParameters1, double chiSquare2, int nParameters2, int nPoints, double significanceLevel) 
public static ArrayList<Object> testOfAdditionalTerms(double chiSquare1, int nParameters1, double chiSquare2, int nParameters2, int nPoints)  
public static ArrayList<Object> testOfAdditionalTerms_ArrayList(double chiSquare1, int nParameters1, double chiSquare2, int nParameters2, int nPoints, double significanceLevel)  
public static ArrayList<Object> testOfAdditionalTerms_ArrayList(double chiSquare1, int nParameters1, double chiSquare2, int nParameters2, int nPoints)  
public static double testOfAdditionalTermsFratio(double chiSquare1, int nParameters1, double chiSquare2, int nParameters2, int nPoints)  
public static double testOfAdditionalTermsFprobability(double chiSquare1, int nParameters1, double chiSquare2, int nParameters2, int nPoints)  
Resetting the denominator in statistical formulae 
public static void setDenominatorToN()  
public static void setDenominatorToNminusOne()  
Reset Stat class continued fraction evaluation parameters This evaluation is called by the Regression statistical analysis 
public static void resetCFmaxIter(int maxit)  
public static int getCFmaxIter()  
public static void resetCFtolerance(double tolerance)  
public static double getCFtolerance() 
RegressionDerivativeFunction  RegressionDerivativeFunction2  
RegressionFunction  YES  NO 
RegressionFunction2  NO  YES 
RegressionFunction3  YES  YES 
RegressionDerivativeFunction  RegressionDerivativeFunction2  
RegressionFunction  YES  NO 
RegressionFunction2  NO  YES 
RegressionFunction3  YES  YES 
Stat.betaFunction  is called by  class BetaFunction 
Stat.betaPDF  is called by  fitBeta 
Stat.chiSquareProb  is called by  getReducedChiSquare linearPrint nonLinearPrint noParameters 
Stat.corrCoeff  is called by  generalLinearStats linearPrint nonLinearPrint noParameters 
Stat.factorial  is called by  class PoissonFunction 
Stat.fTestProb  is called by  linearPrint nonLinearPrint testOfAdditionalTerms_ArrayList 
Stat.gammaFunction  is called by  class GammaFunction 
Stat.gammaPDF  is called by  fitGamma fitErlang 
Stat.getCFmaxIter  is called by  getCFmaxIter 
Stat.getCFtolerance  is called by  getCFtolerance 
Stat.linearCorrCoeffProb  is called by  linearPrint nonLinearPrint noParameters 
Stat.logFactorial  is called by  fitPoisson 
Stat.mean  is called by  fitSigmoidThreshold fitsigmoidHillSips fitEC50 fitlogEC50 fitRectangularHyperbola fitStepFunction 
Stat.resetCFmaxIter  is called by  resetCFmaxIter 
Stat.resetCFtolerance  is called by  resetCFtolerance 
Stat.setStaticDenominatorToN  is called by  setDenominatorToN 
Stat.setStaticDenominatorToNminusOne  is called by  setDenominatorToNminusOne 
Stat.studentTcdf  is called by  generalLinearStats pseudoLinearStats 