public void Simulation(
Parameter1 p,
ArrayList qtl,
double[] env,
double[][] w,
double[][] m,
ArrayList sm,
boolean isSelectMarker) {
Param1 = p;
QTL = qtl;
map = new double[m.length][];
for (int i = 0; i < map.length; i++) {
map[i] = new double[m[i].length];
System.arraycopy(m[i], 0, map[i], 0, map[i].length);
}
weight = new double[w.length][];
for (int i = 0; i < w.length; i++) {
weight[i] = new double[w[i].length];
System.arraycopy(w[i], 0, weight[i], 0, weight[i].length);
}
for (int i = 0; i < map.length; i++) {
Num_interval += map[i].length == 1 ? map[i].length : map[i].length - 1;
}
if (sm != null) {
selectedMarker = sm;
} else {
if (isSelectMarker) {
selectMarker();
}
}
correctLogBon();
makeFullMap(QTL, map);
setup(QTL, map, Param1);
SimulationResults = new ArrayList();
for (int i_rep = 0; i_rep < Param1.rep; i_rep++) {
if (Param1.pt.compareTo("F2") == 0) {
ap =
new F2Population(
Param1.populationSize,
Param1.pheNum.length,
Param1.pt,
distance,
Param1.seed + i_rep,
Param1.mu,
env,
Param1.sd,
QTL,
Param1.mf);
} else if (Param1.pt.compareTo("DH") == 0) {
ap =
new DHPopulation(
Param1.populationSize,
Param1.pheNum.length,
Param1.pt,
distance,
Param1.seed + i_rep,
Param1.mu,
env,
Param1.sd,
QTL,
Param1.mf);
} else {
ap =
new BackCrossPopulation(
Param1.populationSize,
Param1.pheNum.length,
Param1.pt,
distance,
Param1.seed + i_rep,
Param1.mu,
env,
Param1.sd,
QTL,
Param1.mf);
}
ap.ProducePopulation();
for (int i = 0; i < Param1.pheNum.length; i++) {
ap.ProducePhenotype(i, Param1.MU, Param1.T);
}
PrintStream cim = null;
try {
cim = new PrintStream(new BufferedOutputStream(new FileOutputStream("CIM.mcd")));
} catch (Exception e) {
}
ap.CIMFormat(cim);
cim.close();
gs = new GenomeScan(ap, Param1.step);
gs.CalculateIPP();
if (Param1.permutation > 0 && i_rep == 0) {
PrintStream PoutPW = null;
PrintStream PoutPL = null;
PrintStream PoutW = null;
PrintStream PoutL = null;
try {
PoutW = new PrintStream(new BufferedOutputStream(new FileOutputStream("PermuWald.txt")));
PoutL = new PrintStream(new BufferedOutputStream(new FileOutputStream("PermuLN.txt")));
PoutPW =
new PrintStream(new BufferedOutputStream(new FileOutputStream("PermuWaldP.txt")));
PoutPL = new PrintStream(new BufferedOutputStream(new FileOutputStream("PermuLNP.txt")));
} catch (Exception E) {
E.printStackTrace(System.err);
}
thresholdLOD = new ArrayList();
thresholdAP = new ArrayList();
thresholdDP = new ArrayList();
thresholdPwald = new ArrayList();
thresholdPF = new ArrayList();
for (int i_permu = 0; i_permu < Param1.permutation; i_permu++) {
ArrayList IMStatistic = MappingProcedure(i_rep, i_permu + 1);
ArrayList IMLOD = new ArrayList();
ArrayList IMAT = new ArrayList();
ArrayList IMDT = new ArrayList();
ArrayList IMAP = new ArrayList();
ArrayList IMDP = new ArrayList();
ArrayList IMPWald = new ArrayList();
ArrayList IMPF = new ArrayList();
int c = 0;
int ct = 0;
int bt = 0;
for (int j = 0; j < IMStatistic.size(); j++) {
PointMappingStatistic pms = (PointMappingStatistic) IMStatistic.get(j);
PoutPW.print(pms.get_P_wald() + " ");
PoutPL.print(pms.get_P_LN() + " ");
PoutW.print(pms.get_wald() + " ");
PoutL.print(pms.get_LN() + " ");
IMLOD.add(new Double(pms.get_LOD()));
IMAT.add(new Double(pms.get_tStatistic_additive()));
IMDT.add(new Double(pms.get_tStatistic_dominance()));
IMAP.add(new Double(pms.get_logP_additive()));
IMPWald.add(new Double(pms.get_logP_wald()));
IMPF.add(new Double(pms.get_logP_F()));
if (pms.get_logP_additive() > LogBon) {
c++;
}
if (pms.get_P_additive() < BonT) {
bt++;
}
if (weight.length > 1) {
IMDP.add(new Double(pms.get_logP_dominance()));
}
}
PoutPW.println();
PoutPL.println();
PoutW.println();
PoutL.println();
if (c > 0) {
typeI_LogBon += 1;
}
thresholdLOD.add(Collections.max(IMLOD));
thresholdAP.add(Collections.max(IMAP));
if (weight.length > 1) {
thresholdDP.add(Collections.max(IMDP));
}
thresholdPF.add(Collections.max(IMPF));
thresholdPwald.add(Collections.max(IMPWald));
}
PoutPW.close();
PoutPL.close();
PoutW.close();
PoutL.close();
}
ArrayList IMLOD = MappingProcedure(i_rep, 0);
SimulationResults.add(IMLOD);
}
}
public void SummuarySimulation() {
calculatePower();
PrintStream Pout1 = null;
PrintStream Pout2 = null;
PrintStream Pout3 = null;
PrintStream Pout4 = null;
PrintStream Pout5 = null;
PrintStream Pout6 = null;
ChiSquaredDistribution chi = new ChiSquaredDistributionImpl(weight.length);
try {
Pout1 = new PrintStream(new BufferedOutputStream(new FileOutputStream("LogAP.txt")));
Pout2 = new PrintStream(new BufferedOutputStream(new FileOutputStream("LNP.txt")));
Pout3 = new PrintStream(new BufferedOutputStream(new FileOutputStream("LN.txt")));
Pout4 = new PrintStream(new BufferedOutputStream(new FileOutputStream("Wald.txt")));
Pout5 = new PrintStream(new BufferedOutputStream(new FileOutputStream("F.txt")));
Pout6 = new PrintStream(new BufferedOutputStream(new FileOutputStream("LogDP.txt")));
} catch (Exception E) {
E.printStackTrace(System.err);
}
for (int i = 0; i < SimulationResults.size(); i++) {
ArrayList PointStatistics = (ArrayList) SimulationResults.get(i);
double[] LRT = new double[PointStatistics.size()];
double[] WALD = new double[PointStatistics.size()];
DescriptiveStatistics dsLRT = new DescriptiveStatisticsImpl();
DescriptiveStatistics dsWALD = new DescriptiveStatisticsImpl();
SimpleRegression sr = new SimpleRegression();
for (int j = 0; j < PointStatistics.size(); j++) {
PointMappingStatistic pms = (PointMappingStatistic) PointStatistics.get(j);
double lod = pms.get_LOD() > 0 ? pms.get_LOD() : 0;
double ln = pms.get_LN();
double p = 0;
try {
p = chi.cumulativeProbability(ln);
} catch (Exception E) {
E.printStackTrace(System.err);
}
double logLOD = -1 * Math.log10(1 - p);
Pout1.print(pms.get_logP_additive() + " ");
Pout2.print(logLOD + " ");
Pout3.print(pms.get_LN() + " ");
Pout4.print(pms.get_wald() + " ");
Pout5.print(pms.get_logP_F() + " ");
Pout6.print(pms.get_logP_dominance() + " ");
dsLRT.addValue(pms.get_LN());
dsWALD.addValue(pms.get_wald());
LRT[j] = pms.get_LN();
WALD[j] = pms.get_wald();
sr.addData(pms.get_LN(), pms.get_wald());
}
System.out.println(
dsLRT.getMean()
+ " +- "
+ dsLRT.getStandardDeviation()
+ " "
+ dsWALD.getMean()
+ " +- "
+ dsWALD.getStandardDeviation()
+ " cor "
+ sr.getR());
dsLRT.clear();
dsWALD.clear();
sr.clear();
Pout1.println();
Pout2.println();
Pout3.println();
Pout4.println();
Pout5.println();
Pout6.println();
}
Pout1.close();
Pout2.close();
Pout3.close();
Pout4.close();
Pout5.close();
Pout6.close();
}