/// <summary>
/// Does Arian's PCA-ANOVA idea.
/// </summary>
public double PcaAnova(IntensityMatrix source, Peak peak, Core core, List <GroupInfo> types, List <int> replicates)
{
Range times = GetOverlappingTimeRange(core, source, types);
// Create a matrix thusly:
// Control Replicate 1: <day1> <day2> <day3> ...
// Control Replicate 2: <day1> <day2> <day3> ...
// Control Replicate 3: <day1> <day2> <day3> ...
// Drought Replicate 1: <day1> <day2> <day3> ...
// Drought Replicate 2: <day1> <day2> <day3> ...
// ...
// Create and clear the matrix
int rowCount = types.Count * replicates.Count;
int colCount = times.Count;
double[,] matrix = new double[rowCount, colCount];
for (int r = 0; r < rowCount; r++)
{
for (int c = 0; c < colCount; c++)
{
matrix[r, c] = double.NaN;
}
}
// Create the group vector
double[] groups = new double[rowCount];
for (int r = 0; r < rowCount; r++)
{
groups[r] = types[r / replicates.Count].Order;
}
IReadOnlyList <double> raw = source.Find(peak).Values; // TODO: Dirty
// Fill out the values we know
for (int i = 0; i < core.Observations.Count; i++)
{
ObservationInfo o = core.Observations[i];
int typeIndex = types.IndexOf(o.Group);
int repIndex = replicates.IndexOf(o.Rep);
if (times.Contains(o.Time) && typeIndex != -1 && repIndex != -1)
{
int timeIndex = o.Time - times.Min;
int row = typeIndex * replicates.Count + repIndex;
UiControls.Assert(double.IsNaN(matrix[row, timeIndex]), "Duplicate day/time/rep observations in dataset are not allowed.");
matrix[row, timeIndex] = raw[i];
}
}
// Guess missing values
for (int r = 0; r < rowCount; r++)
{
for (int c = 0; c < colCount; c++)
{
if (double.IsNaN(matrix[r, c]))
{
// Missing values - average other values for this point
int repIndex = r % replicates.Count;
int typeStart = r - repIndex;
double total = 0;
int count = 0;
for (int rep = 0; rep < replicates.Count; rep++)
{
int newRow = typeStart + rep;
if (!double.IsNaN(matrix[newRow, c]))
{
total += matrix[newRow, c];
count += 1;
}
}
matrix[r, c] = total / count;
}
}
}
// Now do that R stuff...
var rMatrix = _r.CreateNumericMatrix(matrix);
var rVector = _r.CreateNumericVector(groups);
_r.SetSymbol("a", rMatrix);
_r.SetSymbol("g", rVector);
//R.Evaluate("write.csv(a, file = \"E:/MJR/Project/05. PEAS/AbstressData/Leaf/Positive/CCor/LP1131.cs.csv\")");
try
{
double result = _r.Evaluate(
@"p = prcomp(a)
f = data.frame(y = p$x[,1], group = factor(g))
fit = lm(y ~ group, f)
an = anova(fit)
pval = an$""Pr(>F)""[1]").AsNumeric()[0];
return(result);
}
catch (Exception ex)
{
throw new Exception("Something went wrong calculating PCA-ANOVA statistics. See inner exception for details. (Note that this error can occur if only 1 replicate is specified and PCA-ANOVA is calculated with missing values - make sure the replicates are specified correctly.)", ex);
}
}
/// <summary>
/// Generates the preview image.
/// </summary>
/// <param name="sel">Correction to generate the preview for</param>
private void GeneratePreview(ArgsCorrection sel)
{
// No error unless otherwise
this._lnkError.Visible = false;
// No selection, no preview
if (sel == null)
{
this.SetPreviewError("Nothing to preview", null);
return;
}
// No peak, no preview
if (this._selectedPeak == null)
{
this.SetPreviewError("No peak selected", null);
return;
}
// Title
this._lblPreviewTitle.Text = "Preview (" + this._selectedPeak.DisplayName + ")";
// Get source matrix
IntensityMatrix source = sel.SourceProvider?.Provide;
if (source == null)
{
this.SetPreviewError(StrRes.NoPreview, StrRes.MissingSourceDetails(sel));
return;
}
// Get vectors
Vector original = source.Find(this._selectedPeak);
Vector trend;
Vector corrected;
ConfigurationCorrection temp = new ConfigurationCorrection()
{
Args = sel
};
try
{
IReadOnlyList <ObservationInfo> order;
double[] trendData = temp.ExtractTrend(this._core, original.Values, out order);
trend = (trendData != null) ? new Vector(trendData, original.Header, order.Select(z => new IntensityMatrix.ColumnHeader(z)).ToArray()) : null;
corrected = new Vector(temp.Calculate(this._core, original.Values), original.Header, original.ColHeaders);
}
catch (Exception ex)
{
this._chartOrig.Plot(null);
this._chartChanged.Plot(null);
this._lnkError.Text = StrRes.PreviewError;
this._lnkError.Tag = ex.ToString();
this._lnkError.Visible = true;
return;
}
// Special flag for batch ordering
bool isBatchMode;
if (sel.IsUsingTrend)
{
isBatchMode = sel.Mode == ECorrectionMode.Batch;
}
else
{
isBatchMode = false;
}
// Create displays
StylisedPeak oldDisplay = new StylisedPeak(this._selectedPeak)
{
OverrideDefaultOptions = new StylisedPeakOptions(this._core)
{
ShowAcqisition = isBatchMode,
ViewBatches = this._vBatches,
ViewGroups = this._vTypes,
ConditionsSideBySide = true,
ShowPoints = true,
ShowTrend = sel.IsUsingTrend,
ShowRanges = false,
},
ForceTrend = trend
};
StylisedPeak newDisplay = new StylisedPeak(this._selectedPeak)
{
OverrideDefaultOptions = oldDisplay.OverrideDefaultOptions.Clone(),
ForceObservations = corrected
};
newDisplay.OverrideDefaultOptions.ShowTrend = false;
// Draw it!
this._chartOrig.Plot(oldDisplay);
this._chartChanged.Plot(newDisplay);
}
public void Plot(StylisedPeak stylisedPeak)
{
// Get observations
Vector vector;
if (stylisedPeak.ForceObservations != null)
{
vector = stylisedPeak.ForceObservations;
}
else
{
IntensityMatrix matrix = this._core.Options.SelectedMatrix;
if (matrix == null)
{
vector = null;
}
else
{
vector = matrix.Find(stylisedPeak.Peak);
}
}
Debug.WriteLine("PeakPlot: " + stylisedPeak);
Dictionary <string, MCharting.Series> seriesNames = new Dictionary <string, MCharting.Series>();
Peak peak = stylisedPeak?.Peak;
// Clear plot
MCharting.Plot plot = this.PrepareNewPlot(stylisedPeak != null && !stylisedPeak.IsPreview, peak, vector?.Source);
try // <- CompletNewPlot
{
// Get selection
this.SelectedPeak = peak;
this.SetCaption("Plot of {0}.", peak);
if (peak == null)
{
return;
}
// Get options
StylisedPeakOptions opts = stylisedPeak.OverrideDefaultOptions ?? new StylisedPeakOptions(this._core);
// Get order data
ObservationInfo[] obsOrder = this._core.Observations.ToArray();
// Group legends
IEnumerable <GroupInfoBase> order = opts.ShowAcqisition ? (IEnumerable <GroupInfoBase>)opts.ViewBatches : (IEnumerable <GroupInfoBase>)opts.ViewGroups;
Dictionary <GroupInfoBase, MCharting.Series> groupLegends = this.DrawLegend(plot, order);
if (vector == null)
{
return;
}
// Show acquisition and batches?
if (opts.ShowAcqisition)
{
// --- RAW DATA (points) ---
MCharting.Series legendEntry = new MCharting.Series();
legendEntry.Name = "Observations";
legendEntry.Style.DrawPoints = new SolidBrush(Color.Black);
plot.LegendEntries.Add(legendEntry);
this.AddToPlot(plot, peak, seriesNames, vector, "Raw data", opts, EPlot.ByBatch, groupLegends, legendEntry);
// --- TREND (thick line) ---
if (stylisedPeak.ForceTrend != null)
{
MCharting.Series legendEntry2 = new MCharting.Series();
legendEntry2.Name = "Trend";
legendEntry2.Style.DrawLines = new Pen(Color.Black, this._core.Options.LineWidth);
legendEntry2.Style.DrawLines.Width = 4;
plot.LegendEntries.Add(legendEntry2);
this.AddToPlot(plot, peak, seriesNames, stylisedPeak.ForceTrend, "Trend data", opts, EPlot.ByBatch | EPlot.DrawLine | EPlot.DrawBold, groupLegends, legendEntry2);
}
this.DrawLabels(plot, opts.ConditionsSideBySide, order, opts.DrawExperimentalGroupAxisLabels);
return;
}
// Sort data
ConfigurationTrend trend = opts.SelectedTrend;
Vector avg = stylisedPeak.ForceTrend ?? trend.CreateTrend(this._core, vector);
Vector min = MinSmoother.CreateTrend(this._core, vector);
Vector max = MaxSmoother.CreateTrend(this._core, vector);
// --- PLOT MEAN & SD (lines across)
if (opts.ShowVariableMean)
{
this.AddMeanAndSdLines(plot, opts, vector.Values, peak, groupLegends);
}
// --- RANGE (shaded area) ---
if (opts.ShowRanges)
{
this.AddUpperAndLowerShade(plot, opts, seriesNames, peak, min, max, groupLegends);
}
// --- RAW DATA (points) ---
if (opts.ShowPoints && !stylisedPeak.IsPreview)
{
MCharting.Series legendEntry = new MCharting.Series();
legendEntry.Name = "Observations";
legendEntry.Style.DrawPoints = new SolidBrush(Color.Black);
plot.LegendEntries.Add(legendEntry);
this.AddToPlot(plot, peak, seriesNames, vector, "Raw data", opts, EPlot.None, groupLegends, legendEntry);
}
// --- RANGE (lines) ---
if (opts.ShowMinMax)
{
MCharting.Series legendEntry = new MCharting.Series();
legendEntry.Name = "Range min/max";
legendEntry.Style.DrawLines = new Pen(Color.Gray, this._core.Options.LineWidth);
plot.LegendEntries.Add(legendEntry);
this.AddToPlot(plot, peak, seriesNames, min, "Min value", opts, EPlot.DrawLine, groupLegends, legendEntry);
this.AddToPlot(plot, peak, seriesNames, max, "Max value", opts, EPlot.DrawLine, groupLegends, legendEntry);
}
// --- TREND (thick line) ---
if (opts.ShowTrend)
{
MCharting.Series legendEntry = new MCharting.Series();
legendEntry.Name = "Trend";
legendEntry.Style.DrawLines = new Pen(Color.Black, this._core.Options.LineWidth);
legendEntry.Style.DrawLines.Width = this._core.Options.LineWidth * 4;
plot.LegendEntries.Add(legendEntry);
this.AddToPlot(plot, peak, seriesNames, avg, "Trend data", opts, EPlot.DrawLine | EPlot.DrawBold, groupLegends, legendEntry);
}
// --- LABELS ---
this.DrawLabels(plot, opts.ConditionsSideBySide, order, opts.DrawExperimentalGroupAxisLabels);
}
finally
{
this.CompleteNewPlot(plot);
}
}
