public void Load(string dataType)
{
DataType = dataType;
var regions = _regionService.GetRegionsByIndex();
var filtSubs = _computeService.GetFilteredSubjectsByComputeGroup();
var grp1 = filtSubs[ComputeGroup.GroupOne];
var grp2 = filtSubs[ComputeGroup.GroupTwo];
List<List<double>> vtxStrs1 = new List<List<double>>();
List<List<double>> vtxStrs2 = new List<List<double>>();
foreach (var r in regions)
{
vtxStrs1.Add(new List<double>());
vtxStrs2.Add(new List<double>());
}
// Calc our global and vertex strengths for our groups
List<double> grp1Vals = CalculateStrength(grp1, dataType, vtxStrs1);
List<double> grp2Vals = CalculateStrength(grp2, dataType, vtxStrs2);
// Sig test the verticies
_rsvms = new List<RegionalStrengthViewModel>();
for (int i = 0; i < regions.Count; i++)
{
var lst1 = vtxStrs1[i];
var lst2 = vtxStrs2[i];
double ybt = 0; double ylt = 0; double yrt = 0;
alglib.studentttest2(lst1.ToArray(), lst1.Count, lst2.ToArray(), lst2.Count, out ybt, out ylt, out yrt);
RegionalStrengthViewModel rsvm = new RegionalStrengthViewModel
{
ROI = regions[i],
GroupDifference = lst1.Average() - lst2.Average(),
PValue = ybt,
};
_rsvms.Add(rsvm);
}
int idx = 1;
bool begin = true;
var sortedRsvms = _rsvms.OrderBy(r => r.PValue);
foreach (var rsvm in sortedRsvms)
{
double dIdx = (double)idx;
double dReg = (double)regions.Count;
double qval = (dIdx / dReg) * 0.05;
rsvm.SignificantLevel = qval;
if (rsvm.PValue < qval && begin)
rsvm.Significant = true;
else
{
begin = false;
rsvm.Significant = false;
}
idx++;
}
AXPlotModel = LoadPlotModel(_rsvms, r => r.X, r => r.Y);
SGPlotModel = LoadPlotModel(_rsvms, r => (100 - r.Y), r => r.Z);
CRPlotModel = LoadPlotModel(_rsvms, r => r.X, r => r.Z);
double bt = 0; double lt = 0; double rt = 0;
alglib.mannwhitneyutest(grp1Vals.ToArray(), grp1Vals.Count, grp2Vals.ToArray(), grp2Vals.Count, out bt, out lt, out rt);
double tbt = 0; double tlt = 0; double trt = 0;
alglib.studentttest2(grp1Vals.ToArray(), grp1Vals.Count, grp2Vals.ToArray(), grp2Vals.Count, out tbt, out tlt, out trt);
GrpDiff = (grp1Vals.Average() - grp2Vals.Average()).ToString("0.0000");
PValue = "p" + tbt.ToString("0.0000") + " u" + bt.ToString("0.0000");
var model = new PlotModel() { IsLegendVisible = false };
model.PlotMargins = new OxyThickness(0, 0, 0, 0);
model.Axes.Add(new CategoryAxis("", "Group 1", "Group 2"));
model.Axes.Add(new LinearAxis(AxisPosition.Left) { MinimumPadding = 0.1, MaximumPadding = 0.1, IntervalLength = 20 });
var s1 = new BoxPlotSeries
{
Title = "BoxPlotSeries",
Fill = OxyColors.White,
Stroke = OxyColors.DarkBlue,
StrokeThickness = 2,
OutlierSize = 2,
BoxWidth = 0.4
};
s1.Items.Add(MakeBoxPlotItem(0, grp1Vals));
s1.Items.Add(MakeBoxPlotItem(1, grp2Vals));
model.Series.Add(s1);
GlobalPlotModel = model;
}
public void Load(string dataType)
{
DataType = dataType;
var regions = _regionService.GetRegionsByIndex();
var filtSubs = _computeService.GetFilteredSubjectsByComputeGroup();
var grp1 = filtSubs[ComputeGroup.GroupOne];
var grp2 = filtSubs[ComputeGroup.GroupTwo];
List <List <double> > vtxStrs1 = new List <List <double> >();
List <List <double> > vtxStrs2 = new List <List <double> >();
foreach (var r in regions)
{
vtxStrs1.Add(new List <double>());
vtxStrs2.Add(new List <double>());
}
// Calc our global and vertex strengths for our groups
List <double> grp1Vals = CalculateStrength(grp1, dataType, vtxStrs1);
List <double> grp2Vals = CalculateStrength(grp2, dataType, vtxStrs2);
// Sig test the verticies
_rsvms = new List <RegionalStrengthViewModel>();
for (int i = 0; i < regions.Count; i++)
{
var lst1 = vtxStrs1[i];
var lst2 = vtxStrs2[i];
double ybt = 0; double ylt = 0; double yrt = 0;
alglib.studentttest2(lst1.ToArray(), lst1.Count, lst2.ToArray(), lst2.Count, out ybt, out ylt, out yrt);
RegionalStrengthViewModel rsvm = new RegionalStrengthViewModel
{
ROI = regions[i],
GroupDifference = lst1.Average() - lst2.Average(),
PValue = ybt,
};
_rsvms.Add(rsvm);
}
int idx = 1;
bool begin = true;
var sortedRsvms = _rsvms.OrderBy(r => r.PValue);
foreach (var rsvm in sortedRsvms)
{
double dIdx = (double)idx;
double dReg = (double)regions.Count;
double qval = (dIdx / dReg) * 0.05;
rsvm.SignificantLevel = qval;
if (rsvm.PValue < qval && begin)
{
rsvm.Significant = true;
}
else
{
begin = false;
rsvm.Significant = false;
}
idx++;
}
AXPlotModel = LoadPlotModel(_rsvms, r => r.X, r => r.Y);
SGPlotModel = LoadPlotModel(_rsvms, r => (100 - r.Y), r => r.Z);
CRPlotModel = LoadPlotModel(_rsvms, r => r.X, r => r.Z);
double bt = 0; double lt = 0; double rt = 0;
alglib.mannwhitneyutest(grp1Vals.ToArray(), grp1Vals.Count, grp2Vals.ToArray(), grp2Vals.Count, out bt, out lt, out rt);
double tbt = 0; double tlt = 0; double trt = 0;
alglib.studentttest2(grp1Vals.ToArray(), grp1Vals.Count, grp2Vals.ToArray(), grp2Vals.Count, out tbt, out tlt, out trt);
GrpDiff = (grp1Vals.Average() - grp2Vals.Average()).ToString("0.0000");
PValue = "p" + tbt.ToString("0.0000") + " u" + bt.ToString("0.0000");
var model = new PlotModel()
{
IsLegendVisible = false
};
model.PlotMargins = new OxyThickness(0, 0, 0, 0);
model.Axes.Add(new CategoryAxis("", "Group 1", "Group 2"));
model.Axes.Add(new LinearAxis(AxisPosition.Left)
{
MinimumPadding = 0.1, MaximumPadding = 0.1, IntervalLength = 20
});
var s1 = new BoxPlotSeries
{
Title = "BoxPlotSeries",
Fill = OxyColors.White,
Stroke = OxyColors.DarkBlue,
StrokeThickness = 2,
OutlierSize = 2,
BoxWidth = 0.4
};
s1.Items.Add(MakeBoxPlotItem(0, grp1Vals));
s1.Items.Add(MakeBoxPlotItem(1, grp2Vals));
model.Series.Add(s1);
GlobalPlotModel = model;
}
