protected void RevvingUp() {
ChenBase.LineUpGenotypePhenotype lineup = new ChenBase.LineUpGenotypePhenotype();
Hashtable<String, BFamilyStruct> Fam = PedData.getFamilyStruct();
PersonTable.ensureCapacity(qualified_Unrelated + qualified_Sib);
if (PhenoData != null) CovariateTable.ensureCapacity(qualified_Unrelated + qualified_Sib);
status = new double[qualified_Unrelated + qualified_Sib];
ArrayList<PersonIndex> u_P = NewIt.newArrayList();
ArrayList<ArrayList<String>> u_C = NewIt.newArrayList();
ArrayList<PersonIndex> s_P = NewIt.newArrayList();
ArrayList<ArrayList<String>> s_C = NewIt.newArrayList();
ArrayList<Integer> SibIdx = NewIt.newArrayList();
int c = 0;
int s = 0;
int un = 0;
for (String fi : PedData.getFamListSorted()) {
if ((lineup.num_qualified[c][0] + lineup.num_qualified[c][1]) == 0) {
c++;
continue;
}
BFamilyStruct fs = Fam.get(fi);
FamilyUnit FamUnit = PhenoData == null ? null : PhenoData.getFamilyUnit(fi);
String[] pi = fs.getPersonListSorted();
int si = 0;
for (int i = 0; i < pi.length; i++) {
if (!lineup.filter[c][i]) continue;
BPerson per = fs.getPerson(pi[i]);
Subject sub = PhenoData == null ? null : FamUnit.getSubject(pi[i]);
if (fs.hasAncestor(per.getPersonID())) {
s_P.add(new PersonIndex(fs.getFamilyStructName(), pi[i], per, false, false));
status[s + qualified_Unrelated] = Double.parseDouble(per.getAffectedStatus());
if (PhenoData != null) s_C.add(sub.getTraits());
si++;
s++;
} else {
u_P.add(new PersonIndex(fs.getFamilyStructName(), pi[i], per, false, true));
status[un] = Double.parseDouble(per.getAffectedStatus());
if (PhenoData != null) u_C.add(sub.getTraits());
un++;
}
}
if (si != 0) SibIdx.add(new Integer(si));
c++;
}
PersonTable.addAll(u_P);
PersonTable.addAll(s_P);
if (PhenoData != null) {
CovariateTable.addAll(u_C);
CovariateTable.addAll(s_C);
}
numSib = ArrayUtils.toPrimitive(SibIdx.toArray(new Integer[0]));
System.err.println(un + " unrelated individuals.");
Test.LOG.append(un + " unrelated individuals." + "\n");
System.err.println(s + " siblings from " + numSib.length + " families.\n");
Test.LOG.append(s + " siblings from " + numSib.length + " families." + "\n");
}
/** @author Guo-Bo Chen, [email protected] */ public final class SimulationPower { private static String model = "0,3"; private HashMap<String, MDRStatistic> result; private double[] threshold; private double p_001 = 0.01; private double t_001; private int p_001_idx = 0; private double p_005 = 0.05; private double t_005; private int p_005_idx = 0; public static int typeI_005; public static int typeI_001; public static int power_005; public static int power_001; private ArrayList<SimulationStatistic> result_005 = NewIt.newArrayList(); private ArrayList<SimulationStatistic> result_001 = NewIt.newArrayList(); public class SimulationStatistic { protected String model; private double TestingBalancedAccuracy; private double TrainingBalancedAccuracy; private double p_value; public SimulationStatistic(String m, double tra, double ta, double p) { model = m; TestingBalancedAccuracy = tra; TrainingBalancedAccuracy = ta; p_value = p; } public String toString() { NumberFormat number = new DecimalFormat("#.####"); StringBuilder sb = new StringBuilder(); sb.append( model + " " + number.format(TestingBalancedAccuracy) + " " + number.format(TrainingBalancedAccuracy) + " " + number.format(p_value) + System.getProperty("line.separator")); return sb.toString(); } } public SimulationPower(HashMap<String, MDRStatistic> r, double[] t) { result = r; threshold = t; Arrays.sort(threshold); p_005_idx = (int) (threshold.length * (1 - p_005)); p_001_idx = (int) (threshold.length * (1 - p_001)); t_005 = threshold[p_005_idx]; t_001 = threshold[p_001_idx]; } public void calculatePower() { for (Entry<String, MDRStatistic> entry : result.entrySet()) { String k = entry.getKey(); MDRStatistic v = entry.getValue(); double ta = v.getTestingBalancedAccuracy(); if (ta > t_005) { result_005.add( new SimulationStatistic( k, v.getTestingBalancedAccuracy(), v.getTrainingBalancedAccuracy(), getP(p_005_idx, ta))); } if (ta > t_001) { result_001.add( new SimulationStatistic( k, v.getTestingBalancedAccuracy(), v.getTrainingBalancedAccuracy(), getP(p_001_idx, ta))); } } if (result_005.size() > 0) { typeI_005++; for (SimulationStatistic s : result_005) { if (s.model.compareTo(model) == 0) { power_005++; } } } if (result_001.size() > 0) { typeI_001++; for (SimulationStatistic s : result_001) { if (s.model.compareTo(model) == 0) { power_001++; } } } } private double getP(int idx, double t) { double p = idx; for (int i = idx, len = threshold.length; i < len; i++) { if (t > threshold[i]) p++; } return 1 - p / threshold.length; } public String toString() { DecimalFormat fmt = new DecimalFormat("#.###E0"); StringBuilder sb = new StringBuilder(); sb.append(threshold.length + " rounds of permutation:"); sb.append(System.getProperty("line.separator")); sb.append("significance level at alpha = 0.05, BTA threshold=" + fmt.format(t_005)); sb.append(System.getProperty("line.separator")); if (result_005.size() > 0) { for (SimulationStatistic sr : result_005) { sb.append(sr); } } else { sb.append("null"); sb.append(System.getProperty("line.separator")); } sb.append("significance level at alpha = 0.01, BTA threshold=" + fmt.format(t_001)); sb.append(System.getProperty("line.separator")); if (result_001.size() > 0) { for (SimulationStatistic sr : result_001) { sb.append(sr); } } else { sb.append("null"); sb.append(System.getProperty("line.separator")); } return sb.toString(); } }