internal YGen GenYLocal(double[] muWorker, double mu, double sigma)
{
YGen rep = YGen.GetEmptyObject();
if (this.parentModel.ME.Any)
{
//TODO non implanté
return(null);
}
else
{
if (this.logNormalDistrn)
{
if (Data.AnyGT)
{
int[] workerIds = DFRecordByMeasureType[Measure.MeasureType.GreaterThan].Select(rec => rec.WorkerOrdinal).ToArray();
double[] means = muWorker.Extract(workerIds).Add(mu);
int len = this.Data.LogGT.Length;
rep.GT = new double[len];
rep.LogGT = new double[len];
for (int i = 0; i < len; i++)
{
rep.LogGT[i] = RNorm4CensoredMeasures.RNormCensored(means[i], sigma, lower: this.Data.LogGT[i]);
}
rep.GT = rep.LogGT.Exp();
}
if (Data.AnyLT)
{
int[] workerIds = DFRecordByMeasureType[Measure.MeasureType.LessThan].Select(rec => rec.WorkerOrdinal).ToArray();
double[] means = muWorker.Extract(workerIds).Add(mu);
int len = this.Data.LogLT.Length;
rep.LT = new double[len];
rep.LogLT = new double[len];
for (int i = 0; i < len; i++)
{
rep.LogLT[i] = RNorm4CensoredMeasures.RNormCensored(means[i], sigma, upper: this.Data.LogLT[i]);
}
rep.LT = rep.LogLT.Exp();
}
if (Data.AnyIntervalCensored)
{
int[] workerIds = DFRecordByMeasureType[Measure.MeasureType.Interval].Select(rec => rec.WorkerOrdinal).ToArray();
double[] means = muWorker.Extract(workerIds).Add(mu);
int len = this.Data.LogIntervalGT.Length;
rep.I = new double[len];
rep.LogI = new double[len];
for (int i = 0; i < len; i++)
{
rep.LogI[i] = RNorm4CensoredMeasures.RNormCensored(means[i], sigma, lower: this.Data.LogIntervalGT[i], upper: this.Data.LogIntervalLT[i]);
}
rep.I = rep.LogI.Exp();
}
}
else
{
if (Data.AnyGT)
{
int[] workerIds = DFRecordByMeasureType[Measure.MeasureType.GreaterThan].Select(rec => rec.WorkerOrdinal).ToArray();
double[] means = muWorker.Extract(workerIds).Add(mu);
int len = this.Data.GT.Length;
rep.GT = new double[len];
for (int i = 0; i < len; i++)
{
rep.GT[i] = RNorm4CensoredMeasures.RNormCensored(means[i], sigma, lower: this.Data.GT[i]);
}
}
if (Data.AnyLT)
{
int[] workerIds = DFRecordByMeasureType[Measure.MeasureType.LessThan].Select(rec => rec.WorkerOrdinal).ToArray();
double[] means = muWorker.Extract(workerIds).Add(mu);
int len = this.Data.LT.Length;
rep.LT = new double[len];
for (int i = 0; i < len; i++)
{
rep.LT[i] = RNorm4CensoredMeasures.RNormCensored(means[i], sigma, upper: this.Data.LT[i]);
}
}
if (Data.AnyIntervalCensored)
{
int[] workerIds = DFRecordByMeasureType[Measure.MeasureType.Interval].Select(rec => rec.WorkerOrdinal).ToArray();
double[] means = muWorker.Extract(workerIds).Add(mu);
int len = this.Data.IntervalGT.Length;
rep.I = new double[len];
rep.LogI = new double[len];
for (int i = 0; i < len; i++)
{
rep.I[i] = RNorm4CensoredMeasures.RNormCensored(means[i], sigma, lower: this.Data.IntervalGT[i], upper: this.Data.IntervalLT[i]);
}
}
}
}
return(rep);
}//# end of y.gen.local
internal override void Run()
{
ChainNamePair b_s;
b_s = Mcmc.GetChainNames("muOverall");
double[] muOverallBurnin = Result.Chains.GetChain(b_s.Burnin);
double[] muOverallSample = Result.Chains.GetChain(b_s.Sample);
b_s = Mcmc.GetChainNames("sigmaWithin");
double[] sigmaWithinBurnin = Result.Chains.GetChain(b_s.Burnin);
double[] sigmaWithinSample = Result.Chains.GetChain(b_s.Sample);
b_s = Mcmc.GetChainNames("sigmaBetween");
double[] sigmaBetweenBurnin = Result.Chains.GetChain(b_s.Burnin);
double[] sigmaBetweenSample = Result.Chains.GetChain(b_s.Sample);
double[][] workerBurnin = new double[Data.NWorkers][];
double[][] workerSample = new double[Data.NWorkers][];
int iTag = 0;
foreach (string tag in Data.WorkersByTag.Keys)
{
b_s = Mcmc.GetWorkerChainNames(tag);
workerBurnin[iTag] = Result.Chains.GetChain(b_s.Burnin);
workerSample[iTag] = Result.Chains.GetChain(b_s.Sample);
iTag++;
}
int iter = -1, savedIter = 0;
try
{
double logSigmaWithinSD = 1 / Math.Sqrt(LogSigmaWithinPrec);
double logSigmaBetweenSD = 1 / Math.Sqrt(LogSigmaBetweenPrec);
//# Prepare dens.gen.icdf objects
if (this.ME.Any)
{
//o.tv < -truevalue.gen.object(me, outcome.is.logNormally.distributed)
}
if (this.ME.ThroughCV && !this.OutcomeIsLogNormallyDistributed)
{
//o.mu.overall < -mu.truncatedData.gen.local.object(data$N, data$worker$count)
//o.mu.worker < -mu.worker.truncatedData.gen.object(data$worker$count)
}
GenObject oSB = null, oSW = null;
if (this.UseUniformPriorOnSds)
{
if (Data.NWorkers <= 1)
{
oSB = new Sigma_woLM_GenObject(Data.NWorkers);
}
else
{
oSB = null;
}
}
else
{
oSB = new Sigma_LM_GenObject(Data.NWorkers, this.LogSigmaBetweenMu, logSigmaBetweenSD);
}
if (this.ME.ThroughCV && !this.OutcomeIsLogNormallyDistributed)
{
//if (use.uniform.prior.on.sds)
//{
// o.sw < -sigma.within.truncatedData.gen.object(data$N, data$worker$count, T, range = sigma.within.range)
//}
//else
//{
// o.sw < -sigma.within.truncatedData.gen.object(data$N, data$worker$count, F, lnorm.mu = log.sigma.within.mu, lnorm.sd = log.sigma.within.sd)
//}
}
else
{
if (this.UseUniformPriorOnSds)
{
if (Data.N <= 1)
{
oSW = new Sigma_woLM_GenObject(Data.N);
}
else
{
oSW = null;
}
}
else
{
oSW = new Sigma_LM_GenObject(this.Data.N, lNormMu: this.LogSigmaWithinMu, lNormSd: logSigmaWithinSD);
}
}
if (this.ME.Any && !this.ME.Known)
{
//o.me < -me.gen.object(me, outcome.is.logNormally.distributed, data$N)
}
double muOverall = this.InitMuOverall;
double sigmaWithin = InitSigmaWithin;
//# Initialize measured values for subjects with censored values [new_0.10]
YGen genY = YGen.GetEmptyObject();
WorkerData workerData = new WorkerData(this);
this.ResultParams = new Object[1];
this.ResultParams[0] = this.Data.WorkersByTag.Keys;
if (this.Data.AnyCensored)
{
genY = YGen.Inits(data: this.Data, mu: muOverall, sigma: sigmaWithin, meThroughCV: false, logNormalDistrn: OutcomeIsLogNormallyDistributed);
workerData.UpdateGeneratedValues(genY);
}
double[] muWorker = workerData.MuWorker;
workerData.AdjustMuOverall(this.MuOverallLower, this.MuOverallUpper);
muOverall = workerData.MuOverall;
muWorker = muWorker.Substract(muOverall); // # center mu.worker
double[] predicted = workerData.GetPredictedMeans(muOverall);
sigmaWithin = workerData.GetSigmaWithin();
int nIterations = NBurnin + NIter * NThin;
for (iter = 0; iter < nIterations; iter++)
{
if (this.ME.Any)
{
//true.values < -truevalues.gen.local(gen.y, data, me, mu.worker, o = o.tv)
}
//# Sample y values for censored observations
if (this.Data.AnyCensored)
{
//function(true.values, data, me, mu.worker, mu=mu.overall, sigma=sigma.within, logNormal.distrn=outcome.is.logNormally.distributed)
genY = workerData.GenYLocal(muWorker, muOverall, sigmaWithin);
workerData.UpdateGeneratedValues(genY);
}
double[] yWorkerAvg = workerData.MuWorker;
double yAvg = workerData.Average;
//# Sample from f(sigma.within | other parms)
double[] residuals = workerData.GetGenValues().Substract(muWorker.Extract(workerData.WorkerIds)).Substract(muOverall);
double b = residuals.Sqr().ToArray().Sum() / 2.0;
SGNFnAParam localA = null;
if (this.ME.ThroughCV && !this.OutcomeIsLogNormallyDistributed)
{
//A < -c(o.sw$A, list(b = b, mu = mu.overall, muk = mu.worker))
//sigma.within < -dens.gen.icdf(o.sw, A, range = o.sw$range, start = sigma.within, inestimable.lower.limit = o.sw$inestimable.lower.limit)
}
else
{
if (this.UseUniformPriorOnSds)
{
sigmaWithin = WebExpoFunctions3.SqrtInvertedGammaGen(Data.N, b, SigmaWithinRange.Copy(), oSW);
}
else
{
localA = oSW.A.Clone();
localA.B = b;
localA.Mu = muOverall;
localA.Muk = muWorker;
Icdf icdf = new Icdf(oSW, localA, range: Tools.Combine(0, double.PositiveInfinity));
sigmaWithin = icdf.Bidon(start: oSW.Start(localA), inestLowerLim: true);
}
}
//# Sample from f(sigma.between | other parms)
b = muWorker.Sqr().Sum() / 2.0;
double sigmaBetween = 0;
if (UseUniformPriorOnSds)
{
sigmaBetween = WebExpoFunctions3.SqrtInvertedGammaGen(Data.NWorkers, b, this.SigmaBetweenRange.Copy(), oSB);
}
else
{
localA = oSB.A.Clone();
localA.B = b;
Icdf icdf = new Icdf(oSB, localA, range: Tools.Combine(0, double.PositiveInfinity));
sigmaBetween = icdf.Bidon(start: oSB.Start(localA), inestLowerLim: true);
}
//# Sample from f(mu.overall | other parms)
//# modif_0.10
double tmpMean = yAvg - (muWorker.Multiply(this.Data.WorkerCounts).Sum() / this.Data.N);
if (this.ME.ThroughCV && !OutcomeIsLogNormallyDistributed)
{
//muOverall = mu.truncatedData.gen.local(o.mu.overall, tmp.mean, sigma.within, mu.worker, mu.overall.range, current.value = mu.overall)
}
else
{
double tmpSD = sigmaWithin / Math.Sqrt(this.Data.N);
muOverall = RNorm4CensoredMeasures.RNormCensored(tmpMean, tmpSD, lower: this.MuOverallLower, upper: this.MuOverallUpper);
}
//# Sample from f(mu.worker's | other parms)
//# modif_0.10
double[] sigma2A = Tools.Rep(Math.Pow(sigmaWithin, 2.0), this.Data.NWorkers).Divide(this.Data.WorkerCounts); // # vector of length '# of workers'
double sigma2B = Math.Pow(sigmaBetween, 2); // # scalar
double[] muA = yWorkerAvg.Substract(muOverall); // # vector of length '# of workers'
double[] mukStar = muA.Multiply(sigma2B).Divide(sigma2A.Add(sigma2B)); // # vector of length '# of workers'
double[] s2kStar = sigma2A.Multiply(sigma2B).Divide(sigma2A.Add(sigma2B)); // # vector of length '# of workers'
if (this.ME.ThroughCV && !OutcomeIsLogNormallyDistributed)
{
//muWorker = mu.worker.truncatedData.gen(o.mu.worker, muk.star, s2k.star, mu.overall, sigma.within, mu.worker)
}
else
{
muWorker = NormalDistribution.RNorm(this.Data.NWorkers, mu: mukStar, sigma: s2kStar.Sqrt());
}
if (iter < NBurnin)
{
if (MonitorBurnin)
{
muOverallBurnin[iter] = muOverall;
sigmaBetweenBurnin[iter] = sigmaBetween;
sigmaWithinBurnin[iter] = sigmaWithin;
SaveWorkerChainData(iter, muWorker, workerBurnin);
}
}
else if ((iter - NBurnin) % NThin == 0)
{
muOverallSample[savedIter] = muOverall;
sigmaBetweenSample[savedIter] = sigmaBetween;
sigmaWithinSample[savedIter] = sigmaWithin;
SaveWorkerChainData(savedIter, muWorker, workerSample);
savedIter++;
}
} //for ...
}
catch (Exception ex)
{
this.Result.Messages.AddError(WEException.GetStandardMessage(ex, iter, Result.PRNGSeed), this.ClassName);
return;
}
}