private void SetNoFit(string comment = "Not Fit")
{
this.ForEach(x => x.UsedInFit = false);
ValidDataSet = false;
ExpFit = null;
LinFit = null;
pGrowthRate.GrowthRate = Double.NaN;
pGrowthRate.NumPoints = 0;
pGrowthRate.R2 = Double.NaN;
pGrowthRate.RMSE = Double.NaN;
pGrowthRate.DisplayFittingUsed = "Not Fit";
pGrowthRate.Notes = comment;
OnPropertyChange(new PropertyChangedEventArgs("FittedValues"));
OnPropertyChange(new PropertyChangedEventArgs("FittedLogValues"));
}
public static void LinearModelOutlierDetector(GrowthCurve GC)
{
if (!GC.ExpModelFitted)
{
foreach (GrowthDataPoint gdp in GC)
{
if (gdp.UsedInFit)
{
gdp.OutlierFlag = true;
}
}
}
List <double> XvaluesToDrop = new List <double>();
while (true)
{
bool goAgain = false;
double[] xfittedStart = GC.FittedXValues;
double[] yfittedStart = GC.FittedYValues;
//Flag it all
if (xfittedStart.Length < 4)
{
foreach (GrowthDataPoint gdp in GC)
{
if (gdp.UsedInFit)
{
gdp.OutlierFlag = true;
}
}
}
for (int i = 0; i < xfittedStart.Length; i++)
{
var xfit = xfittedStart.ToList();
var yfit = yfittedStart.ToList();
xfit.RemoveAt(i);
yfit.RemoveAt(i);
double curX = xfittedStart[i];
ExponentialFit EXP = new ExponentialFit(xfit.ToArray(), yfit.ToArray());
double xmean = xfit.Average();
double n = (double)xfit.Count;
//Equations from Sleuth p. 190
DoubleArray xs = DoubleArray.From(xfit);
DoubleArray ys = DoubleArray.From(yfit);
double denom = (n - 1) * xs.Std();
double denom2 = 1 / n;
double Difs = xfittedStart[i] - xmean;
Difs = Difs * Difs;
Difs = Difs / denom;
Difs = Difs + denom2 + 1.0;
Difs = Math.Sqrt(Difs) * measError;
double SEPred = Math.Sqrt(Math.Pow(measError, 2.0) + Difs);
////assume mean is 0 so just divide
double predict = EXP.MakePredictionsAtPoints(new double[] { xfittedStart[i] }).First();
////Leading to the following estimated differences
double Error = (predict - yfittedStart[i]) / SEPred;
//ArrayMathInPlace.Abs(Error);
////Giving the probability
Error = ShoNS.MathFunc.ArrayMath.CdfNorm(DoubleArray.From(new List <double>()
{
Error
}))[0];
if (Error > .99)
{
var q = from xx in GC where xx.ODValue == curX select xx;
q.First().OutlierFlag = true;
goAgain = true;
break;
}
}
if (!goAgain)
{
break;
}
}
GC.FitData();
}
/// <summary>
/// Function fits data, should be called everytime the fit changes
/// </summary>
public void FitData()
{
ValidDataSet = true;
double[] XtoFit, YtoFit;
XtoFit = FittedXValues;
YtoFit = FittedYValues;
//MixtureErrorModel = null;
ExpFit = null;
LinFit = null;
if (XtoFit.Length >= 2)
{
//Linear Fit
LinFit = new LinearFit(XtoFit, FittedLogYValues);
pGrowthRate.DisplayFittingUsed = "Linear";
pGrowthRate.GrowthRate = LinFit.Slope;
pGrowthRate.R2 = LinFit.R2;
pGrowthRate.RMSE = LinFit.RMSE;
pGrowthRate.FitterUsed = LinFit;
//Exponential Fit and others
if (XtoFit.Length > 2 && USE_EXPONENTIAL_FITTING)
{
ExpFit = new ExponentialFit(XtoFit, YtoFit);
if (ExpFit.SuccessfulFit)
{
pGrowthRate.DisplayFittingUsed = "Exponential";
pGrowthRate.GrowthRate = ExpFit.GrowthRate;
pGrowthRate.R2 = ExpFit.R2;
pGrowthRate.RMSE = ExpFit.RMSE;
pGrowthRate.FitterUsed = ExpFit;
//(ExpFit.Y.Max() - ExpFit.Y.Min()) > .1
if (ExpFit.Y.Length > 5)
{
this.OffSetExp = new OffSetExponentialFit(ExpFit.X, ExpFit.Y, this[0].ODValue);
#if !MONO
this.LogisticModel = new LogisticModel(ExpFit.X, ExpFit.Y);
#endif
}
else
{
this.OffSetExp = null;
}
}
else
{
this.OffSetExp = null;
}
#if !MONO
QuadModel = new QuadraticLinearRegression(XtoFit, YtoFit);
#endif
}
//Now Set the Growth Rate
pGrowthRate.NumPoints = FittedXValues.Length;
OnPropertyChange(new PropertyChangedEventArgs("FittedValues"));
OnPropertyChange(new PropertyChangedEventArgs("FittedLogValues"));
}
else
{
SetNoFit();
}
}