public IntervalEstimation IntervalDistribution(double[] xArray, int[] vArray, double reponseProbability, double confidenceLevel) { var outputParameters = MLS_getMLS(xArray, vArray); MLR_polar.Likelihood_Ratio_Polar(xArray, vArray, "normal", outputParameters.μ0_final, outputParameters.σ0_final, reponseProbability, confidenceLevel, out var final_result); return(IntervalEstimation.Parse(final_result)); }
private IntervalEstimation GetIntervalEstimationValue(IntervalEstimation rt) { rt.Confidence.Down = StandardSelection.ProcessValue(rt.Confidence.Down); rt.Confidence.Up = StandardSelection.ProcessValue(rt.Confidence.Up); rt.Mu.Down = StandardSelection.ProcessValue(rt.Mu.Down); rt.Mu.Up = StandardSelection.ProcessValue(rt.Mu.Up); return(rt); }
public static IntervalEstimation Parse(double[] finalResult) { IntervalEstimation ret = new IntervalEstimation(); ret.Confidence.Down = finalResult[5]; ret.Confidence.Up = finalResult[4]; ret.Mu.Down = finalResult[1]; ret.Mu.Up = finalResult[0]; ret.Sigma.Down = finalResult[3]; ret.Sigma.Up = finalResult[2]; return(ret); }
public override IntervalEstimation IntervalDistribution(double[] xArray, int[] vArray, double reponseProbability, double confidenceLevel) { MLR_polar.Max_Likelihood_Estimate(xArray, vArray, "logistic", out var mu, out var sigma, out var L); MLR_polar.Likelihood_Ratio_Polar(xArray, vArray, "logistic", mu, sigma, reponseProbability, confidenceLevel, out var final_result); IntervalEstimation ret = new IntervalEstimation(); ret.Confidence.Down = final_result[5]; ret.Confidence.Up = final_result[4]; ret.Mu.Down = final_result[1]; ret.Mu.Up = final_result[0]; ret.Sigma.Down = final_result[3]; ret.Sigma.Up = final_result[2]; return(ret); }
public override IntervalEstimation IntervalDistribution(double[] xArray, int[] vArray, double reponseProbability, double confidenceLevel) { OutputParameters outputParameters = new OutputParameters(); pub_function.norm_MLS_getMLS(xArray, vArray, out outputParameters.varmu, out outputParameters.varsigma, out outputParameters.Maxf, out outputParameters.Mins); MLR_polar.Likelihood_Ratio_Polar(xArray, vArray, "normal", outputParameters.varmu, outputParameters.varsigma, reponseProbability, confidenceLevel, out var final_result); IntervalEstimation ret = new IntervalEstimation(); ret.Confidence.Down = final_result[5]; ret.Confidence.Up = final_result[4]; ret.Mu.Down = final_result[1]; ret.Mu.Up = final_result[0]; ret.Sigma.Down = final_result[3]; ret.Sigma.Up = final_result[2]; return(ret); }
public SideReturnData BatchIntervalCalculate(double Y_Ceiling, double Y_LowerLimit, int Y_PartitionNumber, double ConfidenceLevel, double favg, double fsigma, double[] xArray, int[] vArray, int intervalChoose) { SideReturnData sideReturnData = new SideReturnData(); double Y_ScaleLength = (DistributionSelection.QnormAndQlogisDistribution(Y_Ceiling) - DistributionSelection.QnormAndQlogisDistribution(Y_LowerLimit)) / Y_PartitionNumber; sideReturnData.responseProbability = new double[Y_PartitionNumber + 1]; for (int i = 0; i <= Y_PartitionNumber; i++) { if (i == 0) { sideReturnData.responseProbability[i] = Y_LowerLimit; } else { sideReturnData.responseProbability[i] = DistributionSelection.PointIntervalDistribution(DistributionSelection.QnormAndQlogisDistribution(Y_LowerLimit) + i * Y_ScaleLength, 0, 1); } } sideReturnData.Y_Ceilings = new double[sideReturnData.responseProbability.Length]; sideReturnData.Y_LowerLimits = new double[sideReturnData.responseProbability.Length]; sideReturnData.responsePoints = new double[sideReturnData.responseProbability.Length]; for (int i = 0; i < sideReturnData.responseProbability.Length; i++) { IntervalEstimation ie = new IntervalEstimation(); if (intervalChoose == 0) { ie = SingleSideEstimation(xArray, vArray, sideReturnData.responseProbability[i], ConfidenceLevel); } else { ie = DoubleSideEstimation(xArray, vArray, sideReturnData.responseProbability[i], ConfidenceLevel); } sideReturnData.Y_LowerLimits[i] = ie.Confidence.Down; sideReturnData.Y_Ceilings[i] = ie.Confidence.Up; double fq = sideReturnData.responseProbability[i]; sideReturnData.responsePoints[i] = StandardSelection.ProcessValue(StandardSelection.InverseProcessValue(favg) + (DistributionSelection.QnormAndQlogisDistribution(fq) * fsigma)); } return(sideReturnData); }
public IntervalEstimation IntervalDistribution(double[] xArray, int[] vArray, double reponseProbability, double confidenceLevel) { MLR_polar.Max_Likelihood_Estimate(xArray, vArray, "logistic", out var mu, out var sigma, out var L); MLR_polar.Likelihood_Ratio_Polar(xArray, vArray, "logistic", mu, sigma, reponseProbability, confidenceLevel, out var final_result); return(IntervalEstimation.Parse(final_result)); }