try {
row = data.size(); //numBins
column = data[0].size(); //numGroups in subset
- secondGroupingStart = secGroupingStart; //g
+ secondGroupingStart = secGroupingStart; //g number of samples in group 1
vector< vector<double> > Pmatrix; Pmatrix.resize(row);
for (int i = 0; i < row; i++) { Pmatrix[i].resize(column, 0.0); } // the relative proportion matrix
for (int i = 0; i < column; i++) {
for (int j = 0; j < row; j++) {
Pmatrix[j][i] = data[j][i]/totals[i];
-
}
}
//#************************************************************
//# generate p values fisher's exact test
//#************************************************************
- double total1, total2;
+ double total1, total2; total1 = 0; total2 = 0;
//total for first grouping
for (int i = 0; i < secondGroupingStart; i++) { total1 += totals[i]; }
T_statistics[i] = xbar_diff/denom; // calculate two sample t-statistic
}
- /*for (int i = 0; i < row; i++) {
+ /*for (int i = 0; i < row; i++) {
for (int j = 0; j < 3; j++) {
cout << "C1[" << i+1 << "," << j+1 << "]=" << C1[i][j] << ";" << endl;
cout << "C2[" << i+1 << "," << j+1 << "]=" << C2[i][j] << ";" << endl;
}
cout << "T_statistics[" << i+1 << "]=" << T_statistics[i] << ";" << endl;
+ }
+
+ for (int i = 0; i < row; i++) {
+ for (int j = 0; j < column; j++) {
+ cout << "Fmatrix[" << i+1 << "," << j+1 << "]=" << data[i][j] << ";" << endl;
+ }
}*/
+
//#*************************************
//# generate initial permuted p-values
//#*************************************
//# generate p values for sparse data
//# using fisher's exact test
//#*************************************
- double total1, total2;
+ double total1, total2; total1 = 0; total2 = 0;
//total for first grouping
- for (int i = 0; i < secondGroupingStart; i++) { total1 += totals[i]; }
+ for (int i = 0; i < secondGroupingStart; i++) { total1 += totals[i]; }
//total for second grouping
- for (int i = secondGroupingStart; i < column; i++) { total2 += totals[i]; }
+ for (int i = secondGroupingStart; i < column; i++) { total2 += totals[i]; }
vector<double> fish; fish.resize(row, 0.0);
vector<double> fish2; fish2.resize(row, 0.0);
for(int j = 0; j < secondGroupingStart; j++) { fish[i] += data[i][j]; }
for(int j = secondGroupingStart; j < column; j++) { fish2[i] += data[i][j]; }
-
- if ((fish[1] < secondGroupingStart) && (fish2[i] < (column-secondGroupingStart))) {
+
+ if ((fish[i] < secondGroupingStart) && (fish2[i] < (column-secondGroupingStart))) {
+
double f11, f12, f21, f22;
f11 = fish[i];
f12 = fish2[i];
if (m->control_pressed) { out.close(); return 0; }
//if there are binlabels use them otherwise count.
- if (m->binLabelsInFile.size() == row) { out << m->binLabelsInFile[i] << '\t'; }
+ if (i < m->currentSharedBinLabels.size()) { out << m->currentSharedBinLabels[i] << '\t'; }
else { out << (i+1) << '\t'; }
out << C1[i][0] << '\t' << C1[i][1] << '\t' << C1[i][2] << '\t' << C2[i][0] << '\t' << C2[i][1] << '\t' << C2[i][2] << '\t' << pvalues[i] << '\t' << qvalues[i] << endl;
for (int j = 0; j < numPermutations; j++) { cleanedpermuted_ttests[j].push_back(permuted_ttests[j][i]); }
}
}
-
+
//#now for each taxa
for (int i = 0; i < row; i++) {
//number of cleanedpermuted_ttests greater than tstat[i]
vector< vector<double> > permutedMatrix = Imatrix;
//randomize columns, ie group abundances.
- for (int i = 0; i < permutedMatrix.size(); i++) { random_shuffle(permutedMatrix[i].begin(), permutedMatrix[i].end()); }
+ map<int, int> randomMap;
+ vector<int> randoms;
+ for (int i = 0; i < column; i++) { randoms.push_back(i); }
+ random_shuffle(randoms.begin(), randoms.end());
+ for (int i = 0; i < randoms.size(); i++) { randomMap[i] = randoms[i]; }
//calc ts
vector< vector<double> > C1; C1.resize(row);
for(int i = 0; i < row; i++){ // for each taxa
//# find the mean of each group
double g1Total = 0.0; double g2Total = 0.0;
- for (int j = 0; j < secondGroupingStart; j++) { g1Total += permutedMatrix[i][j]; }
+ for (int j = 0; j < secondGroupingStart; j++) { g1Total += permutedMatrix[i][randomMap[j]]; }
C1[i][0] = g1Total/(double)(secondGroupingStart);
- for (int j = secondGroupingStart; j < column; j++) { g2Total += permutedMatrix[i][j]; }
+ for (int j = secondGroupingStart; j < column; j++) { g2Total += permutedMatrix[i][randomMap[j]]; }
C2[i][0] = g2Total/(double)(column-secondGroupingStart);
//# find the variance of each group
double g1Var = 0.0; double g2Var = 0.0;
- for (int j = 0; j < secondGroupingStart; j++) { g1Var += pow((permutedMatrix[i][j]-C1[i][0]), 2); }
+ for (int j = 0; j < secondGroupingStart; j++) { g1Var += pow((permutedMatrix[i][randomMap[j]]-C1[i][0]), 2); }
C1[i][1] = g1Var/(double)(secondGroupingStart-1);
- for (int j = secondGroupingStart; j < column; j++) { g2Var += pow((permutedMatrix[i][j]-C2[i][0]), 2); }
+ for (int j = secondGroupingStart; j < column; j++) { g2Var += pow((permutedMatrix[i][randomMap[j]]-C2[i][0]), 2); }
C2[i][1] = g2Var/(double)(column-secondGroupingStart-1);
//# find the std error of each group -std err^2 (will change to std err at end)
C2[i][2] = C2[i][1]/(double)(column-secondGroupingStart);
}
+
+
//#*************************************
//# two sample t-statistics
//#*************************************
/***********************************************************/
vector<double> MothurMetastats::calc_qvalues(vector<double>& pValues) {
try {
-
- /* cout << "x <- c(" << pValues[0];
- for (int l = 1; l < pValues.size(); l++){
- cout << ", " << pValues[l];
- }
- cout << ")\n";*/
-
- int numRows = pValues.size();
+ int numRows = pValues.size();
vector<double> qvalues(numRows, 0.0);
//fill lambdas with 0.00, 0.01, 0.02... 0.95
for (int i = 0; i < numRows; i++){ // for each p-value in order
if (pValues[i] > lambdas[l]){ count++; }
}
- pi0_hat[l] = count/(double)(numRows*(1-lambdas[l]));
+ pi0_hat[l] = count/(double)(numRows*(1.0-lambdas[l]));
+ //cout << lambdas[l] << '\t' << count << '\t' << numRows*(1.0-lambdas[l]) << '\t' << pi0_hat[l] << endl;
}
double pi0 = smoothSpline(lambdas, pi0_hat, 3);
projects / mothur.git / blobdiff