//attname and attvalue generally passed in from a reader
public virtual void SetDemog3Properties(string attName,
string attValue)
{
switch (attName)
{
case cHousing:
this.Housing = attValue;
break;
case cWorkStatus:
this.WorkStatus = attValue;
break;
case cMaritalStatus:
this.MaritalStatus = attValue;
break;
case cLocationId:
this.LocationId = attValue;
break;
case cFamilyIncomeCurrency:
this.FamilyIncomeCurrency = attValue;
break;
case cFamilyIncomePerYear:
this.FamilyIncomePerYear = CalculatorHelpers.ConvertStringToInt(attValue);
break;
default:
break;
}
}
//takes '25_0', and outputs 25 as a double and 0 as an integer
private bool GetValues(string obsMember, out string memberValue,
out int filePosition)
{
bool bHasFileCount = false;
memberValue = string.Empty;
string sValue = obsMember;
filePosition = 0;
int iFileExtensionStart
= obsMember.LastIndexOf(Constants.FILENAME_DELIMITER);
if (iFileExtensionStart != -1)
{
bHasFileCount = true;
sValue = obsMember.Substring(0, iFileExtensionStart);
//get rid of the delimiter
iFileExtensionStart += 1;
string sFileNumber = obsMember.Substring(iFileExtensionStart,
obsMember.Length - iFileExtensionStart);
filePosition = CalculatorHelpers.ConvertStringToInt(sFileNumber);
}
memberValue = sValue;
return(bHasFileCount);
}
private void AddComparisonStatisticProperties(int filePosition,
double attNumber)
{
if (this.CalcParams.AnalyzerParms.BaseCBStatistic.NameIsNotAStatistic(
this.CalcParams.AnalyzerParms.ObservationAttributeName))
{
//for standard deviation's running sum
double dbStandardDeviation = 0;
double dbMemberSquared = Math.Pow(attNumber, 2);
double dbMemberSquaredTotal = 0;
//for mean and sd running sum
//double dbNewValue = attNumber;
int iObservations = 1;
double dbMean = attNumber;
double dbTotal = attNumber;
double dbMedian = 0;
//could be another observation for same element within file
//get the existing values from the collections, add the new
//attnumber to the statistical collection, and set new
//statistics (two tillage operations with same label
//will have two observations, N=2, and need new mean, sd ... set)
//note this attribute will never have a statistical suffix
//in the attribute name (i.e. _MEAN)
string sAttValue = this.GetStatisticalPropertyValue(
filePosition, this.CalcParams.AnalyzerParms.NodePositionIndex,
this.CalcParams.AnalyzerParms.ObservationAttributeName,
this.CalcParams.AnalyzerParms.NodeName, string.Empty);
dbTotal = CalculatorHelpers.ConvertStringToDouble(sAttValue)
+ attNumber;
//get the new N and MEAN
if (!string.IsNullOrEmpty(sAttValue))
{
//set the number of observations
string sNValue = this.GetStatisticalPropertyValue(
filePosition, this.CalcParams.AnalyzerParms.NodePositionIndex,
this.CalcParams.AnalyzerParms.ObservationAttributeName,
this.CalcParams.AnalyzerParms.NodeName, CostBenefitStatistic01.N);
iObservations = CalculatorHelpers.ConvertStringToInt(sNValue) + 1;
//set the mean
dbMean = dbTotal / iObservations;
//set the new variance
string sSumVariance = this.GetStatisticalPropertyValue(
filePosition, this.CalcParams.AnalyzerParms.NodePositionIndex,
this.CalcParams.AnalyzerParms.ObservationAttributeName,
this.CalcParams.AnalyzerParms.NodeName, CostBenefitStatistic01.VAR2);
dbMemberSquared = dbMemberSquared
+ CalculatorHelpers.ConvertStringToDouble(sSumVariance);
}
//get the new variance
dbMemberSquaredTotal += dbMemberSquared;
//get the new standard deviation
dbStandardDeviation = GetStandardDeviation(dbTotal, iObservations, dbMemberSquaredTotal);
//set the corresponding property values in the cb collections
SetStatisticProperties(filePosition,
iObservations, dbTotal, dbMean, dbMemberSquaredTotal,
dbStandardDeviation, dbMedian);
}
}
//attname and attvalue generally passed in from a reader
public void SetCEGeneralProperties(string attName,
string attValue)
{
switch (attName)
{
case OUTPUTS:
this.Outputs
= CalculatorHelpers.ConvertStringToInt(attValue);
break;
case INPUTS:
this.Inputs
= CalculatorHelpers.ConvertStringToInt(attValue);
break;
default:
break;
}
}
//attname and attvalue generally passed in from a reader
public virtual void SetHealthBenefit1Properties(string attName,
string attValue)
{
if (Demographics == null)
{
Demographics = new Demog1();
}
Demographics.SetDemog1Properties(attName, attValue);
switch (attName)
{
case cOutputCost:
this.OutputCost = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cBenefitAdjustment:
this.BenefitAdjustment = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cAdjustedBenefit:
this.AdjustedBenefit = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cOutputEffect1Name:
this.OutputEffect1Name = attValue;
break;
case cOutputEffect1Unit:
this.OutputEffect1Unit = attValue;
break;
case cOutputEffect1Amount:
this.OutputEffect1Amount = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cOutputEffect1Price:
this.OutputEffect1Price = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cOutputEffect1Cost:
this.OutputEffect1Cost = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cAverageBenefitRating:
this.AverageBenefitRating = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cPhysicalHealthRating:
this.PhysicalHealthRating = CalculatorHelpers.ConvertStringToInt(attValue);
break;
case cEmotionalHealthRating:
this.EmotionalHealthRating = CalculatorHelpers.ConvertStringToInt(attValue);
break;
case cSocialHealthRating:
this.SocialHealthRating = CalculatorHelpers.ConvertStringToInt(attValue);
break;
case cEconomicHealthRating:
this.EconomicHealthRating = CalculatorHelpers.ConvertStringToInt(attValue);
break;
case cHealthCareDeliveryRating:
this.HealthCareDeliveryRating = CalculatorHelpers.ConvertStringToInt(attValue);
break;
case cBeforeQOLRating:
this.BeforeQOLRating = CalculatorHelpers.ConvertStringToInt(attValue);
break;
case cAfterQOLRating:
this.AfterQOLRating = CalculatorHelpers.ConvertStringToInt(attValue);
break;
case cBeforeYears:
this.BeforeYears = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cAfterYears:
this.AfterYears = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cAfterYearsProb:
this.AfterYearsProb = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cTimeTradeoffYears:
this.TimeTradeoffYears = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cEquityMultiplier:
this.EquityMultiplier = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cBenefitAssessment:
this.BenefitAssessment = attValue;
break;
case cQALY:
this.QALY = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cICERQALY:
this.ICERQALY = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cTTOQALY:
this.TTOQALY = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cWillDoSurvey:
this.WillDoSurvey = CalculatorHelpers.ConvertStringToBool(attValue);
break;
default:
break;
}
}
public double[] GetSampleData(string distType, double mostLikelyEstimate,
double lowEstimate, double highEstimate)
{
if (Iterations > 10000)
{
Iterations = 10000;
}
if (Iterations <= 2)
{
Iterations = 1000;
}
if (this.CILevel < 10)
{
this.CILevel = 90;
}
if (this.CILevel > 99)
{
this.CILevel = 99;
}
Random rnd = new Random(Random);
mostLikelyEstimate = Math.Round(mostLikelyEstimate, 4);
lowEstimate = Math.Round(lowEstimate, 4);
highEstimate = Math.Round(highEstimate, 4);
var sampledata = new double[Iterations];
if (distType == Calculator1.RUC_TYPES.triangle.ToString())
{
if (lowEstimate >= mostLikelyEstimate || lowEstimate == 0)
{
//arbitrary rules (25%)
lowEstimate = mostLikelyEstimate * .75;
//no errors: lowEstimate = 0 is often the case
//sb.AppendLine(Errors.GetMessage("DATA_BADDISTRIBUTION"));
}
if (highEstimate <= mostLikelyEstimate || highEstimate == 0)
{
//arbitrary rules (25%)
highEstimate = mostLikelyEstimate * 1.25;
}
if (Random != 0)
{
//generate samples of the Triangular(low, high, mode) distribution;
Triangular.Samples(rnd, sampledata, lowEstimate, highEstimate, mostLikelyEstimate);
}
else
{
//generate samples of the Triangular(low, high, mode) distribution;
Triangular.Samples(sampledata, lowEstimate, highEstimate, mostLikelyEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.normal.ToString())
{
//generate samples of the Normal(mean, sd) distribution;
if (Random != 0)
{
Normal.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
Normal.Samples(sampledata, lowEstimate, highEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.lognormal.ToString())
{
if (Random != 0)
{
LogNormal.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
LogNormal.Samples(sampledata, lowEstimate, highEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.weibull.ToString())
{
if (Random != 0)
{
Weibull.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
Weibull.Samples(sampledata, lowEstimate, highEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.beta.ToString())
{
if (Random != 0)
{
Beta.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
Beta.Samples(sampledata, lowEstimate, highEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.pareto.ToString())
{
if (Random != 0)
{
Pareto.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
Pareto.Samples(sampledata, lowEstimate, highEstimate);
}
}
else if (distType == Calculator1.RUC_TYPES.uniform.ToString())
{
var sampleints = new int[Iterations];
int iLower = CalculatorHelpers.ConvertStringToInt(lowEstimate.ToString());
int iUpper = CalculatorHelpers.ConvertStringToInt(highEstimate.ToString());
if (Random != 0)
{
DiscreteUniform.Samples(rnd, sampleints, iLower, iUpper);
}
else
{
DiscreteUniform.Samples(sampleints, iLower, iUpper);
}
for (int i = 0; i < sampleints.Count(); i++)
{
sampledata[i] = sampleints[i];
}
}
else if (distType == Calculator1.RUC_TYPES.bernoulli.ToString())
{
var sampleints = new int[Iterations];
if (Random != 0)
{
Bernoulli.Samples(rnd, sampleints, lowEstimate);
}
else
{
Bernoulli.Samples(sampleints, lowEstimate);
}
for (int i = 0; i < sampleints.Count(); i++)
{
sampledata[i] = sampleints[i];
}
}
else if (distType == Calculator1.RUC_TYPES.poisson.ToString())
{
var sampleints = new int[Iterations];
if (Random != 0)
{
Poisson.Samples(rnd, sampleints, lowEstimate);
}
else
{
Poisson.Samples(sampleints, lowEstimate);
}
for (int i = 0; i < sampleints.Count(); i++)
{
sampledata[i] = sampleints[i];
}
}
else if (distType == Calculator1.RUC_TYPES.binomial.ToString())
{
var sampleints = new int[Iterations];
int iUpperEstimate = CalculatorHelpers.ConvertStringToInt(highEstimate.ToString());
if (Random != 0)
{
Binomial.Samples(rnd, sampleints, lowEstimate, iUpperEstimate);
}
else
{
Binomial.Samples(sampleints, lowEstimate, iUpperEstimate);
}
for (int i = 0; i < sampleints.Count(); i++)
{
sampledata[i] = sampleints[i];
}
}
else if (distType == Calculator1.RUC_TYPES.gamma.ToString())
{
//generate samples of the Gamma(shape, scale) distribution;
if (Random != 0)
{
Gamma.Samples(rnd, sampledata, lowEstimate, highEstimate);
}
else
{
Gamma.Samples(sampledata, lowEstimate, highEstimate);
}
}
else
{
//don't force them to use distribution
}
//hold for possible infernet use
//else if (distType == Calculator1.RUC_TYPES.dirichlet.ToString())
//{
// //generate samples of the Dirichlet(random, alpha) distribution;
// Dirichlet.Sample(sampledata, lowEstimate);
//}
//else if (distType == Calculator1.RUC_TYPES.wishart.ToString())
//{
// //generate samples of the Wishart(random, degrees of freedom, scale) distribution;
// Wishart.Sample(sampledata, lowEstimate, highEstimate);
//}
//the mathlibrary supports more than a dozen additional distributions
return(sampledata);
}
//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);
}
public void SetTotalME2Stat1Property(ME2Stat1 ind,
string attName, string attValue)
{
string sPropertyValue = string.Empty;
switch (attName)
{
case cTME2N:
ind.TME2N = CalculatorHelpers.ConvertStringToInt(attValue);
break;
case cTotalME2MMean:
ind.TotalME2MMean = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cTotalME2MMedian:
ind.TotalME2MMedian = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cTotalME2MVariance:
ind.TotalME2MVariance = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cTotalME2MStandDev:
ind.TotalME2MStandDev = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cTotalME2LMean:
ind.TotalME2LMean = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cTotalME2LMedian:
ind.TotalME2LMedian = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cTotalME2LVariance:
ind.TotalME2LVariance = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cTotalME2LStandDev:
ind.TotalME2LStandDev = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cTotalME2UMean:
ind.TotalME2UMean = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cTotalME2UMedian:
ind.TotalME2UMedian = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cTotalME2UVariance:
ind.TotalME2UVariance = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cTotalME2UStandDev:
ind.TotalME2UStandDev = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
default:
break;
}
}
public virtual void SetMachinery1InputProperty(string attName,
string attValue)
{
switch (attName)
{
case cMarketValue:
this.MarketValue = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cListPriceAdj:
this.ListPriceAdj = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cSalvageValue:
this.SalvageValue = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cFuelAmount:
this.FuelAmount = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cFuelUnit:
this.FuelUnit = attValue;
break;
case cFuelPrice:
this.FuelPrice = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cFuelCost:
this.FuelCost = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cLubeOilAmount:
this.LubeOilAmount = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cLubeOilUnit:
this.LubeOilUnit = attValue;
break;
case cLubeOilPrice:
this.LubeOilPrice = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cLubeOilCost:
this.LubeOilCost = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cRepairCost:
this.RepairCost = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cLaborAmount:
this.LaborAmount = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cLaborUnit:
this.LaborUnit = attValue;
break;
case cLaborPrice:
this.LaborPrice = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cLaborCost:
this.LaborCost = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cServiceUnits:
this.ServiceUnits = attValue;
break;
case cServiceCapacityUnits:
this.ServiceCapacityUnits = attValue;
break;
case cCapitalRecoveryCost:
this.CapitalRecoveryCost = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cTaxesHousingInsuranceCost:
this.TaxesHousingInsuranceCost = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
case cOptionForCapacity:
this.OptionForCapacity = CalculatorHelpers.ConvertStringToInt(attValue);
break;
case cOptionForInflation:
this.OptionForFuel = CalculatorHelpers.ConvertStringToInt(attValue);
break;
case cOptionForTime:
this.OptionForTime = CalculatorHelpers.ConvertStringToInt(attValue);
break;
case cOptionForFuel:
this.OptionForFuel = CalculatorHelpers.ConvertStringToInt(attValue);
break;
case cLaborAmountAdj:
this.LaborAmountAdj = CalculatorHelpers.ConvertStringToDouble(attValue);
break;
default:
break;
}
}