exit(1);
}
}
-
+/*********************************************************************************************************************************/
+double LinearAlgebra::calcKendall(vector<double>& x, vector<double>& y, double& sig){
+ try {
+ if (x.size() != y.size()) { m->mothurOut("[ERROR]: vector size mismatch."); m->mothurOutEndLine(); return 0.0; }
+
+ //format data
+ vector<spearmanRank> xscores;
+ for (int i = 0; i < x.size(); i++) {
+ spearmanRank member(toString(i), x[i]);
+ xscores.push_back(member);
+ }
+
+ //sort xscores
+ sort(xscores.begin(), xscores.end(), compareSpearman);
+
+ //convert scores to ranks of x
+ vector<spearmanRank*> ties;
+ int rankTotal = 0;
+ for (int j = 0; j < xscores.size(); j++) {
+ rankTotal += (j+1);
+ ties.push_back(&(xscores[j]));
+
+ if (j != xscores.size()-1) { // you are not the last so you can look ahead
+ if (xscores[j].score != xscores[j+1].score) { // you are done with ties, rank them and continue
+ for (int k = 0; k < ties.size(); k++) {
+ float thisrank = rankTotal / (float) ties.size();
+ (*ties[k]).score = thisrank;
+ }
+ ties.clear();
+ rankTotal = 0;
+ }
+ }else { // you are the last one
+ for (int k = 0; k < ties.size(); k++) {
+ float thisrank = rankTotal / (float) ties.size();
+ (*ties[k]).score = thisrank;
+ }
+ }
+ }
+
+
+ //format data
+ vector<spearmanRank> yscores;
+ for (int j = 0; j < y.size(); j++) {
+ spearmanRank member(toString(j), y[j]);
+ yscores.push_back(member);
+ }
+
+ //sort yscores
+ sort(yscores.begin(), yscores.end(), compareSpearman);
+
+ //convert to ranks
+ map<string, float> rank;
+ vector<spearmanRank> yties;
+ rankTotal = 0;
+ for (int j = 0; j < yscores.size(); j++) {
+ rankTotal += (j+1);
+ yties.push_back(yscores[j]);
+
+ if (j != yscores.size()-1) { // you are not the last so you can look ahead
+ if (yscores[j].score != yscores[j+1].score) { // you are done with ties, rank them and continue
+ for (int k = 0; k < yties.size(); k++) {
+ float thisrank = rankTotal / (float) yties.size();
+ rank[yties[k].name] = thisrank;
+ }
+ yties.clear();
+ rankTotal = 0;
+ }
+ }else { // you are the last one
+ for (int k = 0; k < yties.size(); k++) {
+ float thisrank = rankTotal / (float) yties.size();
+ rank[yties[k].name] = thisrank;
+ }
+ }
+ }
+
+
+ int numCoor = 0;
+ int numDisCoor = 0;
+
+ //associate x and y
+ vector<spearmanRank> otus;
+ for (int l = 0; l < xscores.size(); l++) {
+ spearmanRank member(xscores[l].name, rank[xscores[l].name]);
+ otus.push_back(member);
+ }
+
+ int count = 0;
+ for (int l = 0; l < xscores.size(); l++) {
+
+ int numWithHigherRank = 0;
+ int numWithLowerRank = 0;
+ float thisrank = otus[l].score;
+
+ for (int u = l+1; u < xscores.size(); u++) {
+ if (otus[u].score > thisrank) { numWithHigherRank++; }
+ else if (otus[u].score < thisrank) { numWithLowerRank++; }
+ count++;
+ }
+
+ numCoor += numWithHigherRank;
+ numDisCoor += numWithLowerRank;
+ }
+
+ double p = (numCoor - numDisCoor) / (float) count;
+
+ //calc signif - zA - http://en.wikipedia.org/wiki/Kendall_tau_rank_correlation_coefficient#Significance_tests
+ double numer = 3.0 * (numCoor - numDisCoor);
+ int n = xscores.size();
+ double denom = n * (n-1) * (2*n + 5) / (double) 2.0;
+ denom = sqrt(denom);
+ sig = numer / denom;
+
+ if (isnan(sig) || isinf(sig)) { sig = 0.0; }
+
+ return p;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "LinearAlgebra", "calcKendall");
+ exit(1);
+ }
+}
+/*********************************************************************************************************************************/
+double LinearAlgebra::calcSpearman(vector<double>& x, vector<double>& y, double& sig){
+ try {
+ if (x.size() != y.size()) { m->mothurOut("[ERROR]: vector size mismatch."); m->mothurOutEndLine(); return 0.0; }
+
+ //format data
+ map<float, int> tableX;
+ map<float, int>::iterator itTable;
+ vector<spearmanRank> xscores;
+
+ for (int i = 0; i < x.size(); i++) {
+ spearmanRank member(toString(i), x[i]);
+ xscores.push_back(member);
+
+ //count number of repeats
+ itTable = tableX.find(x[i]);
+ if (itTable == tableX.end()) {
+ tableX[x[i]] = 1;
+ }else {
+ tableX[x[i]]++;
+ }
+ }
+
+
+ //calc LX
+ double Lx = 0.0;
+ for (itTable = tableX.begin(); itTable != tableX.end(); itTable++) {
+ double tx = (double) itTable->second;
+ Lx += ((pow(tx, 3.0) - tx) / 12.0);
+ }
+
+
+ //sort x
+ sort(xscores.begin(), xscores.end(), compareSpearman);
+
+ //convert scores to ranks of x
+ //convert to ranks
+ map<string, float> rankx;
+ vector<spearmanRank> xties;
+ int rankTotal = 0;
+ for (int j = 0; j < xscores.size(); j++) {
+ rankTotal += (j+1);
+ xties.push_back(xscores[j]);
+
+ if (j != xscores.size()-1) { // you are not the last so you can look ahead
+ if (xscores[j].score != xscores[j+1].score) { // you are done with ties, rank them and continue
+ for (int k = 0; k < xties.size(); k++) {
+ float thisrank = rankTotal / (float) xties.size();
+ rankx[xties[k].name] = thisrank;
+ }
+ xties.clear();
+ rankTotal = 0;
+ }
+ }else { // you are the last one
+ for (int k = 0; k < xties.size(); k++) {
+ float thisrank = rankTotal / (float) xties.size();
+ rankx[xties[k].name] = thisrank;
+ }
+ }
+ }
+
+ //format x
+ vector<spearmanRank> yscores;
+ map<float, int> tableY;
+ for (int j = 0; j < y.size(); j++) {
+ spearmanRank member(toString(j), y[j]);
+ yscores.push_back(member);
+
+ itTable = tableY.find(member.score);
+ if (itTable == tableY.end()) {
+ tableY[member.score] = 1;
+ }else {
+ tableY[member.score]++;
+ }
+
+ }
+
+ //calc Ly
+ double Ly = 0.0;
+ for (itTable = tableY.begin(); itTable != tableY.end(); itTable++) {
+ double ty = (double) itTable->second;
+ Ly += ((pow(ty, 3.0) - ty) / 12.0);
+ }
+
+ sort(yscores.begin(), yscores.end(), compareSpearman);
+
+ //convert to ranks
+ map<string, float> rank;
+ vector<spearmanRank> yties;
+ rankTotal = 0;
+ for (int j = 0; j < yscores.size(); j++) {
+ rankTotal += (j+1);
+ yties.push_back(yscores[j]);
+
+ if (j != yscores.size()-1) { // you are not the last so you can look ahead
+ if (yscores[j].score != yscores[j+1].score) { // you are done with ties, rank them and continue
+ for (int k = 0; k < yties.size(); k++) {
+ float thisrank = rankTotal / (float) yties.size();
+ rank[yties[k].name] = thisrank;
+ }
+ yties.clear();
+ rankTotal = 0;
+ }
+ }else { // you are the last one
+ for (int k = 0; k < yties.size(); k++) {
+ float thisrank = rankTotal / (float) yties.size();
+ rank[yties[k].name] = thisrank;
+ }
+ }
+ }
+
+ double di = 0.0;
+ for (int k = 0; k < x.size(); k++) {
+
+ float xi = rankx[toString(k)];
+ float yi = rank[toString(k)];
+
+ di += ((xi - yi) * (xi - yi));
+ }
+
+ double p = 0.0;
+
+ double n = (double) x.size();
+ double SX2 = ((pow(n, 3.0) - n) / 12.0) - Lx;
+ double SY2 = ((pow(n, 3.0) - n) / 12.0) - Ly;
+
+ p = (SX2 + SY2 - di) / (2.0 * sqrt((SX2*SY2)));
+
+ //signifigance calc - http://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient
+ double temp = (x.size()-2) / (double) (1- (p*p));
+ temp = sqrt(temp);
+ sig = p*temp;
+ if (isnan(sig) || isinf(sig)) { sig = 0.0; }
+
+ return p;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "LinearAlgebra", "calcSpearman");
+ exit(1);
+ }
+}
+/*********************************************************************************************************************************/
+double LinearAlgebra::calcPearson(vector<double>& x, vector<double>& y, double& sig){
+ try {
+ if (x.size() != y.size()) { m->mothurOut("[ERROR]: vector size mismatch."); m->mothurOutEndLine(); return 0.0; }
+
+ //find average X
+ float averageX = 0.0;
+ for (int i = 0; i < x.size(); i++) { averageX += x[i]; }
+ averageX = averageX / (float) x.size();
+
+ //find average Y
+ float sumY = 0.0;
+ for (int j = 0; j < y.size(); j++) { sumY += y[j]; }
+ float Ybar = sumY / (float) y.size();
+
+ double r = 0.0;
+ double numerator = 0.0;
+ double denomTerm1 = 0.0;
+ double denomTerm2 = 0.0;
+
+ for (int j = 0; j < x.size(); j++) {
+ float Yi = y[j];
+ float Xi = x[j];
+
+ numerator += ((Xi - averageX) * (Yi - Ybar));
+ denomTerm1 += ((Xi - averageX) * (Xi - averageX));
+ denomTerm2 += ((Yi - Ybar) * (Yi - Ybar));
+ }
+
+ double denom = (sqrt(denomTerm1) * sqrt(denomTerm2));
+
+ r = numerator / denom;
+
+ //signifigance calc - http://faculty.vassar.edu/lowry/ch4apx.html
+ double temp = (1- (r*r)) / (double) (x.size()-2);
+ temp = sqrt(temp);
+ sig = r / temp;
+ if (isnan(sig) || isinf(sig)) { sig = 0.0; }
+
+ return r;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "LinearAlgebra", "calcPearson");
+ exit(1);
+ }
+}
/*********************************************************************************************************************************/
vector<vector<double> > LinearAlgebra::getObservedEuclideanDistance(vector<vector<double> >& relAbundData){
projects / mothur.git / blobdiff