public Model(MeasureList measures, bool outcomeIsLogNormallyDistributed, McmcParameters mcmcParams)
{
OelStdz = new OelStandardizer(this);
Measures = measures;
this.ClassName = this.GetType().Name;
if (mcmcParams == null)
{
mcmcParams = new McmcParameters(); // default values
this.Messages.AddWarning("Mcmc parameters were undefined, using default values", this.ClassName);
}
this.Messages.Add(measures.Error);
this.Messages.Add(mcmcParams.Error);
this.NIter = mcmcParams.NIter;
this.NBurnin = mcmcParams.NBurnin;
this.NThin = mcmcParams.NThin;
this.MonitorBurnin = mcmcParams.MonitorBurnin;
this.OutcomeIsLogNormallyDistributed = outcomeIsLogNormallyDistributed;
if (this.OutcomeIsLogNormallyDistributed)
{
OelStdz.StandardizeObservations();
}
this.Data = new DataSummary(measures, this.OutcomeIsLogNormallyDistributed);
this.ME = measures.ME;
this.OEL = measures.OEL;
}
public SEGUninformativeModel(MeasureList measures, UninformativeModelParameters specificParams, McmcParameters mcmcParams = null)
: base(measures, specificParams.LogNormalDstrn, /*new Range(specificParams.MuLower, specificParams.MuUpper, specificParams.InitMu),*/ mcmcParams)
{
this.MuLower = specificParams.MuLower;
this.MuUpper = specificParams.MuUpper;
this.SDRange = specificParams.SDRange;
this.InitMu = specificParams.InitMu;
this.InitSD = specificParams.InitSD;
this.Result = new ModelResult(this, "mu", "sd");
bool testSD = double.IsNaN(this.InitSD) || (this.InitSD == 0);
if (Tools.IsND(this.InitMu) || testSD)
{
InitialValues initVals = WebExpoFunctions3.DefaultInits(this.Data, this.OutcomeIsLogNormallyDistributed, Tools.Combine(this.MuLower, this.MuUpper), this.SDRange, includeCensoredData: false);
if (Tools.IsND(this.InitMu))
{
this.InitMu = initVals.Mu;
}
if (testSD)
{
this.InitSD = initVals.SigmaWithin;
}
}
if (this.ME.ThroughCV)
{
this.Result.Chains.Add("cv");
}
}// end constructor
public SEGInformedVarModel(MeasureList measures, SEGInformedVarModelParameters specificParams, McmcParameters mcmcParams = null, PastDataSummary pastDataSummary = null)
: base(measures, specificParams.LogNormalDstrn, mcmcParams)
{
this.PastData = pastDataSummary == null ? PastDataSummary.EmptyObject : pastDataSummary;
this.Messages.Add(this.PastData.Messages);
this.LogSigmaMu = specificParams.LogSigmaMu;
this.LogSigmaPrec = specificParams.LogSigmaPrec;
this.InitSigma = specificParams.InitSigma;
this.MuLower = specificParams.MuLower;
this.MuUpper = specificParams.MuUpper;
this.InitMu = specificParams.InitMu;
this.Result = new ModelResult(this, "mu", "sd");
if (this.ME.ThroughCV)
{
this.Result.Chains.Add("cv");
}
}// end constructor
public BetweenWorkerModel(MeasureList measures, AbstractBWModelParameters specificParams, McmcParameters mcmcParams = null)
: base(measures, specificParams.LogNormalDstrn, mcmcParams)
{
this.Result = new ModelResult(this, "muOverall", "sigmaWithin", "sigmaBetween");
if (!measures.IsWorkerInfoComplete)
{
this.Messages.AddError("Incomplete worker information.", this.ClassName);
}
if (this.Data.MEAny)
{
if (this.Data.METhroughSD && !MeasurementErrorSupport.MESD)
{
this.Messages.AddError("No support for METhrougSD.", this.ClassName);
}
else if (this.Data.METhroughCV && !MeasurementErrorSupport.me_CV)
{
this.Messages.AddError("No support for METhrougCV.", this.ClassName);
}
}
if (this.Messages.Level == Message.Severity.Error)
{
return;
}
this.UseUniformPriorOnSds = specificParams.UseUniformPriorOnSds;
if (this.UseUniformPriorOnSds)
{
this.SigmaBetweenRange = Tools.Copy(((BWModelParameters_UniformPriorOnSDs)specificParams).SigmaBetweenRange);
this.SigmaWithinRange = ((BWModelParameters_UniformPriorOnSDs)specificParams).SigmaWithinRange;
}
else
{
this.LogSigmaBetweenMu = ((BWModelParameters)specificParams).LogSigmaBetweenMu;
this.LogSigmaBetweenPrec = ((BWModelParameters)specificParams).LogSigmaBetweenPrec;
this.LogSigmaWithinMu = ((BWModelParameters)specificParams).LogSigmaWithinMu;
this.LogSigmaWithinPrec = ((BWModelParameters)specificParams).LogSigmaWithinPrec;
}
//4 paramètres communs aux 2 formes que prend le modèle.
this.MuOverallLower = specificParams.MuOverallLower;
this.MuOverallUpper = specificParams.MuOverallUpper;
this.InitMuOverall = specificParams.InitMuOverall;
this.InitSigmaWithin = specificParams.InitSigmaWithin;
this.MuOverallRange = Tools.Combine(this.MuOverallLower, this.MuOverallUpper);
this.Result.AddWorkerMuChains(Data.WorkersByTag.Values);
if (!Data.AnyCensored && UseUniformPriorOnSds)
{
//
//# If there is no variation observed within subjects,
//# then we require a non-null lower bound for sigma.within
//
if (SigmaWithinRange[0] == 0)
{
bool problem = (Data.NWorkers == Data.N); //il devrait donc y avoir plus d'une mesure pour au moins un travailleur
if (!problem)
{
problem = true;
foreach (KeyValuePair <Worker, List <Measure> > kv in Data.MeasuresByWorker)
{
if (kv.Value.Count <= 1)
{
continue;
}
//at least two measures
double vari = kv.Value.Select(x => x.A).Variance();
if (double.IsNaN(vari) || (vari == 0))
{
continue;
}
else
{
problem = false;
break;
}
} //foreach
}
if (problem)
{
this.Messages.AddError("Lower bound for SigmaWithinRange must be > 0 due to observed null within-subjects variance.", this.ClassName);
return;
}
}
}
if (specificParams.GetType() == typeof(BWModelParameters))// && (Tools.IsND(this.InitMuOverall) || Tools.IsND(this.InitSigmaWithin)))
{
InitialValues initValues = specificParams.DefaultInits(this.Data, includeCensoredData: false);
if (Tools.IsND(this.InitMuOverall))
{
this.InitMuOverall = initValues.Mu;
}
if (Tools.IsND(this.InitSigmaWithin))
{
this.InitSigmaWithin = initValues.SigmaWithin;
}
this.NormalizedY = initValues.NormalizedY.ToArray();
}
}