private IntervalEstimation GetIntervalEstimationValue(IntervalEstimation rt)
{
rt.Confidence.Down = LiftingMethodStandardSelection.ProcessValue(rt.Confidence.Down);
rt.Confidence.Up = LiftingMethodStandardSelection.ProcessValue(rt.Confidence.Up);
rt.Mu.Down = LiftingMethodStandardSelection.ProcessValue(rt.Mu.Down);
rt.Mu.Up = LiftingMethodStandardSelection.ProcessValue(rt.Mu.Up);
return(rt);
}
private static int IntervalEstimation(int count, Worksheet sheet, IntervalEstimation ie, IntervalEstimation ie01, double confidenceLevel)
{
sheet.Range["B" + count + ":H" + count + ""].Text = "置信水平" + confidenceLevel + "";
sheet.Range["B" + count + ":H" + count + ""].Merge();
count++;
sheet.Range["B" + count + ":H" + count + ""].Text = "均值区间估计：(" + ie.Mu.Down + "," + ie.Mu.Up + ")";
sheet.Range["B" + count + ":H" + count + ""].Merge();
count++;
sheet.Range["B" + count + ":H" + count + ""].Text = "标准差区间估计：(" + ie.Sigma.Down + "," + ie.Sigma.Up + ")";
sheet.Range["B" + count + ":H" + count + ""].Merge();
count++;
sheet.Range["B" + count + ":H" + count + ""].Text = "响应概率为99.9%区间估计：(" + ie.Confidence.Down + "," + ie.Confidence.Up + ")";
sheet.Range["B" + count + ":H" + count + ""].Merge();
count++;
sheet.Range["B" + count + ":H" + count + ""].Text = "响应概率为0.1%区间估计：(" + ie01.Confidence.Down + "," + ie01.Confidence.Up + ")";
sheet.Range["B" + count + ":H" + count + ""].Merge();
count++;
return(count);
}
//拟然比法区间计算绘图
public SideReturnData QuasiLikelihoodRatioMethod(double Y_Ceiling, double Y_LowerLimit, int Y_PartitionNumber, double ConfidenceLevel, double favg, double fsigma, double[] xArray, int[] vArray, int intervalChoose)
{
var sideReturnData = GetSideReturnDataValue(Y_Ceiling, Y_LowerLimit, Y_PartitionNumber);
xArray = LiftingMethodStandardSelection.InverseProcessArray(xArray);
for (int i = 0; i < sideReturnData.responseProbability.Length; i++)
{
IntervalEstimation ie = new IntervalEstimation();
if (intervalChoose == 0)
{
ie = GetIntervalEstimationValue(SingleSideEstimation(xArray, vArray, sideReturnData.responseProbability[i], ConfidenceLevel, LiftingMethodStandardSelection.InverseProcessValue(favg), fsigma));
}
else
{
ie = GetIntervalEstimationValue(DoubleSideEstimation(xArray, vArray, sideReturnData.responseProbability[i], ConfidenceLevel, LiftingMethodStandardSelection.InverseProcessValue(favg), fsigma));
}
sideReturnData.Y_LowerLimits[i] = ie.Confidence.Down;
sideReturnData.Y_Ceilings[i] = ie.Confidence.Up;
double fq = sideReturnData.responseProbability[i];
sideReturnData.responsePoints[i] = LiftingMethodStandardSelection.ProcessValue(LiftingMethodStandardSelection.InverseProcessValue(favg) + LiftingDistributionSelection.QValue(fq) * fsigma);
}
return(sideReturnData);
}
public IntervalEstimation IntervalDistribution(double[] xArray, int[] vArray, double reponseProbability, double confidenceLevel, double favg, double fsigma)
{
MLR_polar.Likelihood_Ratio_Polar(xArray, vArray, "logistic", favg, fsigma, reponseProbability, confidenceLevel, out var final_result);
return(IntervalEstimation.Parse(final_result));
}
