//this is asych for the calling Task.WhenAll
//but does not necessarily need internal asych awaits
public async Task <bool> RunAlgorithmAsync(List <List <double> > data)
{
bool bHasCalculations = false;
try
{
//minimal data requirement is first five cols
if (_colNames.Count() < 5 ||
_mathTerms.Count() == 0)
{
ErrorMessage = "Complete randomized anova requires at least 1 dependent variable and 1 independent variable. Randomized block and factorial anovas require at least 2 independent variables.";
return(bHasCalculations);
}
if (data.Count() < 5)
{
//185 same as other analysis
ErrorMessage = "Anova requires at least 2 rows of observed data and 3 rows of scoring data.";
return(bHasCalculations);
}
//convert data to a Math.Net Matrix
//last 3 rows are used to generate ci
List <List <double> > dataci = data.Skip(data.Count - _scoreRows).ToList();
data.Reverse();
List <List <double> > dataobs = data.Skip(_scoreRows).ToList();
dataobs.Reverse();
//actual observed values
Vector <double> y = Shared.GetYData(dataobs);
//treatments or factor1 levels
Matrix <double> treatments = Shared.GetDistinctMatrix(dataobs, 1);
//206 condition added due to M and E dataset indexing
_totalsNeeded = treatments.ColumnCount;
double tDF = treatments.ColumnCount - 1;
//step 1. get total of observed data
double yTotal = y.Sum();
//step 2. get total of observed data squared
double yTotal2 = y.PointwisePower(2).Sum();
//step 3. set CM
double CM = Math.Pow(yTotal, 2) / y.Count();
//step 4. treatments and blocks
double SSTotal = yTotal2 - CM;
//add the data to a string builder
StringBuilder sb = new StringBuilder();
sb.AppendLine("anova results");
//5 col array
string[] cols = new string[] { "source", "df", "SS", "MS", "F" };
sb.AppendLine(Shared.GetLine(cols, true));
List <List <double> > totals = new List <List <double> >(_totalsNeeded);
//min treatment block required
bool bIsBlock = (_depColNames.Contains("treatment") && _depColNames.Contains("block")) ? true : false;
//min 2 factors required
bool bIsFactorial = (_depColNames.Contains("factor1") && _depColNames.Contains("factor2")) ? true : false;
bool bIsComplete = (bIsBlock == false && bIsFactorial == false) ? true : false;
if (bIsComplete)
{
double eDF = y.Count() - treatments.ColumnCount;
//step 5.treatments (correct to divide by rows)
double SST = ((treatments.ColumnSums().PointwisePower(2).Sum()) / treatments.RowCount) - CM;
//step 6. error
double SSE = SSTotal - SST;
//step 7. mean treatment
double MST = SST / tDF;
//step 8. mean error
double MSE = SSE / (y.Count() - treatments.ColumnCount);
//step 9. F treatments
double FT = MST / MSE;
//tests
double s = Math.Pow(MSE, 0.5);
//correct 2 tailed t test
int itDF = CalculatorHelpers.ConvertStringToInt(tDF.ToString());
int ieDF = CalculatorHelpers.ConvertStringToInt(eDF.ToString());
double dbCI = CalculatorHelpers.GetConfidenceIntervalProb(_confidenceInt);
//TINV divides dbCI by 2 to get student t
double tCriticalValue = ExcelFunctions.TInv(dbCI, ieDF);
//prevents an error in Finv
if (itDF == 0)
{
itDF = 1;
}
double FCriticalTValue = ExcelFunctions.FInv(dbCI, itDF, ieDF);
string FTGreaterFCritical = (FT > FCriticalTValue) ? "true" : "false";
for (int i = 0; i < _totalsNeeded; i++)
{
//206 condition added due to M and E dataset indexing
if (i < treatments.ColumnCount)
{
SetAnovaIntervals(i, totals, treatments, tCriticalValue, s,
CalculatorHelpers.ConvertStringToDouble(treatments.RowCount.ToString()),
FT, FCriticalTValue, bIsComplete);
}
}
this.DataToAnalyze.Add(Label, totals);
////add the data to a string builder
cols = new string[] { "treats", itDF.ToString("F0"), SST.ToString("F4"), MST.ToString("F4"), FT.ToString("F4") };
sb.AppendLine(Shared.GetLine(cols, false));
cols = new string[] { "error ", ieDF.ToString("F0"), SSE.ToString("F4"), MSE.ToString("F4") };
sb.AppendLine(Shared.GetLine(cols, false));
cols = new string[] { "total ", (y.Count() - 1).ToString("F0"), (SSTotal).ToString("F4") };
sb.AppendLine(Shared.GetLine(cols, false));
cols = new string[] { string.Concat("F Crit ", "treats"), FCriticalTValue.ToString("F5"), "F > F Critical", FTGreaterFCritical };
sb.AppendLine(Shared.GetLine(cols, true));
}
else
{
//observations per cell for factorials (data[0] if first row of data)
double r = Shared.GetObservationsPerCell(dataobs, 1, data[0].ElementAt(1), data[0].ElementAt(2));
//blocks or factor2 levels
Matrix <double> blocks = Shared.GetDistinctMatrix(dataobs, 2);
double bDF = blocks.ColumnCount - 1;
double eDF = y.Count() - treatments.ColumnCount - blocks.ColumnCount + 1;
if (bIsFactorial)
{
eDF = (treatments.ColumnCount * blocks.ColumnCount) * (r - 1);
}
//factorial interaction df
double tbDF = tDF * bDF;
//step 5.treatments (correct to divide by r)
double SST = ((treatments.ColumnSums().PointwisePower(2).Sum()) / (blocks.ColumnCount * r)) - CM;
//step 6. blocks
double SSB = ((blocks.ColumnSums().PointwisePower(2).Sum()) / (treatments.ColumnCount * r)) - CM;
//factor level interaction
double SSFL = 0;
//step 7. error
double SSE = 0;
if (bIsFactorial)
{
double totalinteraction = Shared.GetTotalInteraction(blocks, r);
//watch block.colcount for 2 x 3 factorials
SSFL = (totalinteraction / r) - SST - SSB - CM;
//step 7. error
SSE = SSTotal - SST - SSB - SSFL;
}
else
{
//step 7. error
SSE = SSTotal - SST - SSB;
}
//step 8. mean treatment
double MST = SST / tDF;
//step 9. mean block
double MSB = SSB / bDF;
//step 10. mean error
double MSE = SSE / eDF;
double MSFL = SSFL / tbDF;
//step 11. F treatments
double FT = MST / MSE;
//step 12. F blocks
double FB = MSB / MSE;
double FTB = MSFL / MSE;
//tests
double s = Math.Pow(MSE, 0.5);
//correct 2 tailed t test
int itDF = CalculatorHelpers.ConvertStringToInt(tDF.ToString());
int ibDF = CalculatorHelpers.ConvertStringToInt(bDF.ToString());
int itbDF = CalculatorHelpers.ConvertStringToInt(tbDF.ToString());
int ieDF = CalculatorHelpers.ConvertStringToInt(eDF.ToString());
double dbCI = CalculatorHelpers.GetConfidenceIntervalProb(_confidenceInt);
//TINV divides dbCI by 2 to get student t
double tCriticalValue = ExcelFunctions.TInv(dbCI, ieDF);
//prevents an error in Finv
if (itDF == 0)
{
itDF = 1;
}
double FCriticalTValue = ExcelFunctions.FInv(dbCI, itDF, ieDF);
string FTGreaterFCritical = (FT > FCriticalTValue) ? "true" : "false";
//prevents an error in Finv
if (ibDF == 0)
{
ibDF = 1;
}
double FCriticalBValue = ExcelFunctions.FInv(dbCI, ibDF, ieDF);
string FBGreaterFCritical = (FB > FCriticalBValue) ? "true" : "false";
//prevents an error in Finv
if (itbDF == 0)
{
itbDF = 1;
}
double FCriticalTBValue = ExcelFunctions.FInv(dbCI, itbDF, ieDF);
string FTBGreaterFCritical = (FTB > FCriticalTBValue) ? "true" : "false";
//List<List<double>> totals = new List<List<double>>(_totalsNeeded);
if (bIsFactorial)
{
//unless custom stylesheets are developed, can only display factor 1 - factor 2 diffs
//build a matrix equivalent to treatments -1 row, variable cols
Matrix <double> torbs = Matrix <double> .Build.Dense(1, _totalsNeeded);
List <double> mrow = new List <double>(_totalsNeeded);
for (int i = 0; i < _totalsNeeded; i++)
{
double cellMean = Shared.GetMeanPerCell(dataobs, 1, 2, i, i, r);
mrow.Add(cellMean);
}
torbs.SetRow(0, mrow.ToArray());
for (int i = 0; i < _totalsNeeded; i++)
{
//206 condition added due to M and E dataset indexing
if (i < treatments.ColumnCount)
{
//treatments
SetAnovaIntervals(i, totals, torbs, tCriticalValue, s,
CalculatorHelpers.ConvertStringToDouble(blocks.ColumnCount.ToString()),
FT, FCriticalTValue, bIsComplete);
}
}
//cell1Mean = Shared.GetMeanPerCell(dataobs, 1, 2, 0, 0, r);
//cell2Mean = Shared.GetMeanPerCell(dataobs, 1, 2, 0, 1, r);
//SetAnovaIntervals2(1, cell1Mean, cell2Mean, tCriticalValue, s, r);
//cell1Mean = Shared.GetMeanPerCell(dataobs, 1, 2, 0, 0, r);
//cell2Mean = Shared.GetMeanPerCell(dataobs, 1, 2, 2, 0, r);
//SetAnovaIntervals2(2, cell1Mean, cell2Mean, tCriticalValue, s, r);
//double cell1Mean = Shared.GetMeanPerCell(dataobs, 1, 2, 0, 0, r);
//double cell2Mean = Shared.GetMeanPerCell(dataobs, 1, 2, 1, 0, r);
//SetAnovaIntervals2(0, cell1Mean, cell2Mean, tCriticalValue, s, r);
//cell1Mean = Shared.GetMeanPerCell(dataobs, 1, 2, 0, 0, r);
//cell2Mean = Shared.GetMeanPerCell(dataobs, 1, 2, 0, 1, r);
//SetAnovaIntervals2(1, cell1Mean, cell2Mean, tCriticalValue, s, r);
//cell1Mean = Shared.GetMeanPerCell(dataobs, 1, 2, 0, 0, r);
//cell2Mean = Shared.GetMeanPerCell(dataobs, 1, 2, 2, 0, r);
//SetAnovaIntervals2(2, cell1Mean, cell2Mean, tCriticalValue, s, r);
}
else
{
//unless custom stylesheets are developed, need to only display treatment diffs
for (int i = 0; i < _totalsNeeded; i++)
{
//206 condition added due to M and E dataset indexing
if (i < treatments.ColumnCount)
{
//treatments
SetAnovaIntervals(i, totals, treatments, tCriticalValue, s,
CalculatorHelpers.ConvertStringToDouble(blocks.ColumnCount.ToString()),
FT, FCriticalTValue, bIsComplete);
////blocks
//SetAnovaIntervals(i, totals, blocks, tCriticalValue, s,
// CalculatorHelpers.ConvertStringToDouble(treatments.ColumnCount.ToString()),
// FB, FCriticalBValue, bIsComplete);
//interactions
}
}
}
this.DataToAnalyze.Add(Label, totals);
string sTreats = "treats ";
string sBlocks = "blocks ";
if (bIsFactorial)
{
sTreats = "factor1 ";
sBlocks = "factor2 ";
}
////add the data to a string builder
//StringBuilder sb = new StringBuilder();
//sb.AppendLine("anova results");
cols = new string[] { sTreats, itDF.ToString("F0"), SST.ToString("F4"), MST.ToString("F4"), FT.ToString("F4") };
sb.AppendLine(Shared.GetLine(cols, false));
cols = new string[] { sBlocks, bDF.ToString("F0"), SSB.ToString("F4"), MSB.ToString("F4"), FB.ToString("F4") };
sb.AppendLine(Shared.GetLine(cols, false));
if (bIsFactorial)
{
cols = new string[] { "interacts ", tbDF.ToString("F0"), SSFL.ToString("F4"), MSFL.ToString("F4"), FTB.ToString("F4") };
sb.AppendLine(Shared.GetLine(cols, false));
}
cols = new string[] { "error ", ieDF.ToString("F0"), SSE.ToString("F4"), MSE.ToString("F4") };
sb.AppendLine(Shared.GetLine(cols, false));
cols = new string[] { "total ", (y.Count() - 1).ToString("F0"), (SSTotal).ToString("F4") };
sb.AppendLine(Shared.GetLine(cols, false));
cols = new string[] { string.Concat("F Crit ", sTreats), FCriticalTValue.ToString("F5"), "F > F Critical", FTGreaterFCritical };
sb.AppendLine(Shared.GetLine(cols, true));
cols = new string[] { string.Concat("F Crit ", sBlocks), FCriticalBValue.ToString("F5"), "F > F Critical", FBGreaterFCritical };
sb.AppendLine(Shared.GetLine(cols, true));
if (bIsFactorial)
{
cols = new string[] { "F Crit Interacts", FCriticalTBValue.ToString("F5"), "F > F Critical", FTBGreaterFCritical };
sb.AppendLine(Shared.GetLine(cols, true));
}
}
cols = new string[] { "estimate", "mean diff", string.Concat("lower ", _confidenceInt.ToString(), "%"), string.Concat("upper ", _confidenceInt.ToString(), "%") };
sb.AppendLine(Shared.GetLine(cols, true));
//same report for calculator and analyzer
for (int i = 0; i < _totalsNeeded; i++)
{
if (totals[i].Count >= 5)
{
if (i == 0)
{
QTPredicted = totals[i].ElementAt(0);
QTL = QTPredicted - totals[i].ElementAt(4);
QTU = QTPredicted + totals[i].ElementAt(4);
cols = new string[] { "Treat 1 Mean ", QTPredicted.ToString("F4"), QTL.ToString("F4"), QTU.ToString("F4") };
sb.AppendLine(Shared.GetLine(cols, false));
}
else
{
QTPredicted = totals[i].ElementAt(1);
QTL = QTPredicted - totals[i].ElementAt(2);
QTU = QTPredicted + totals[i].ElementAt(2);
cols = new string[] { string.Concat("xminus1 ", i.ToString(), " "), QTPredicted.ToString("F4"), QTL.ToString("F4"), QTU.ToString("F4") };
sb.AppendLine(Shared.GetLine(cols, false));
QTPredicted = totals[i].ElementAt(3);
QTL = QTPredicted - totals[i].ElementAt(4);
QTU = QTPredicted + totals[i].ElementAt(4);
cols = new string[] { string.Concat("base ", i.ToString(), " "), QTPredicted.ToString("F4"), QTL.ToString("F4"), QTU.ToString("F4") };
sb.AppendLine(Shared.GetLine(cols, false));
}
}
}
if (this.MathResult.ToLower().StartsWith("http"))
{
bool bHasSaved = await CalculatorHelpers.SaveTextInURI(
_params.ExtensionDocToCalcURI, sb.ToString(), this.MathResult);
if (!string.IsNullOrEmpty(_params.ExtensionDocToCalcURI.ErrorMessage))
{
this.MathResult += _params.ExtensionDocToCalcURI.ErrorMessage;
//done with errormsg
_params.ExtensionDocToCalcURI.ErrorMessage = string.Empty;
}
}
else
{
this.MathResult = sb.ToString();
}
bHasCalculations = true;
}
catch (Exception ex)
{
this.ErrorMessage = ex.Message;
}
return(bHasCalculations);
}