*/
#include "linearalgebra.h"
+#include "wilcox.h"
// This class references functions used from "Numerical Recipes in C++" //
exit(1);
}
}
+/*********************************************************************************************************************************/
+double LinearAlgebra::calcKruskalWallis(vector<spearmanRank>& values, double& pValue){
+ try {
+ double H;
+ set<string> treatments;
+
+ //rank values
+ sort(values.begin(), values.end(), compareSpearman);
+ vector<spearmanRank*> ties;
+ int rankTotal = 0;
+ vector<int> TIES;
+ for (int j = 0; j < values.size(); j++) {
+ treatments.insert(values[j].name);
+ rankTotal += (j+1);
+ ties.push_back(&(values[j]));
+
+ if (j != values.size()-1) { // you are not the last so you can look ahead
+ if (values[j].score != values[j+1].score) { // you are done with ties, rank them and continue
+ if (ties.size() > 1) { TIES.push_back(ties.size()); }
+ for (int k = 0; k < ties.size(); k++) {
+ double thisrank = rankTotal / (double) ties.size();
+ (*ties[k]).score = thisrank;
+ }
+ ties.clear();
+ rankTotal = 0;
+ }
+ }else { // you are the last one
+ if (ties.size() > 1) { TIES.push_back(ties.size()); }
+ for (int k = 0; k < ties.size(); k++) {
+ double thisrank = rankTotal / (double) ties.size();
+ (*ties[k]).score = thisrank;
+ }
+ }
+ }
+
+
+ // H = 12/(N*(N+1)) * (sum Ti^2/n) - 3(N+1)
+ map<string, double> sums;
+ map<string, double> counts;
+ for (set<string>::iterator it = treatments.begin(); it != treatments.end(); it++) { sums[*it] = 0.0; counts[*it] = 0; }
+
+ for (int j = 0; j < values.size(); j++) {
+ sums[values[j].name] += values[j].score;
+ counts[values[j].name]+= 1.0;
+ }
+
+ double middleTerm = 0.0;
+ for (set<string>::iterator it = treatments.begin(); it != treatments.end(); it++) {
+ middleTerm += ((sums[*it]*sums[*it])/counts[*it]);
+ }
+
+ double firstTerm = 12 / (double) (values.size()*(values.size()+1));
+ double lastTerm = 3 * (values.size()+1);
+
+ H = firstTerm * middleTerm - lastTerm;
+
+ //adjust for ties
+ if (TIES.size() != 0) {
+ double sum = 0.0;
+ for (int j = 0; j < TIES.size(); j++) { sum += ((TIES[j]*TIES[j]*TIES[j])-TIES[j]); }
+ double result = 1.0 - (sum / (double) ((values.size()*values.size()*values.size())-values.size()));
+ H /= result;
+ }
+
+ //Numerical Recipes pg221
+ pValue = 1.0 - (gammp(((treatments.size()-1)/(double)2.0), H/2.0));
+
+ return H;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "LinearAlgebra", "calcKruskalWallis");
+ exit(1);
+ }
+}
+/*********************************************************************************************************************************/
+//thanks http://www.johndcook.com/cpp_phi.html
+double LinearAlgebra::pnorm(double x){
+ try {
+ // constants
+ double a1 = 0.254829592;
+ double a2 = -0.284496736;
+ double a3 = 1.421413741;
+ double a4 = -1.453152027;
+ double a5 = 1.061405429;
+ double p = 0.3275911;
+
+ // Save the sign of x
+ int sign = 1;
+ if (x < 0)
+ sign = -1;
+ x = fabs(x)/sqrt(2.0);
+
+ // A&S formula 7.1.26
+ double t = 1.0/(1.0 + p*x);
+ double y = 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);
+
+ return 0.5*(1.0 + sign*y);
+ }
+ catch(exception& e) {
+ m->errorOut(e, "LinearAlgebra", "pnorm");
+ exit(1);
+ }
+}
+
+/*********************************************************************************************************************************/
+double LinearAlgebra::calcWilcoxon(vector<double>& x, vector<double>& y, double& sig){
+ try {
+ double W = 0.0;
+ sig = 0.0;
+
+ vector<spearmanRank> ranks;
+ for (int i = 0; i < x.size(); i++) {
+ if (m->control_pressed) { return W; }
+ spearmanRank member("x", x[i]);
+ ranks.push_back(member);
+ }
+
+ for (int i = 0; i < y.size(); i++) {
+ if (m->control_pressed) { return W; }
+ spearmanRank member("y", y[i]);
+ ranks.push_back(member);
+ }
+
+ //sort values
+ sort(ranks.begin(), ranks.end(), compareSpearman);
+
+ //convert scores to ranks of x
+ vector<spearmanRank*> ties;
+ int rankTotal = 0;
+ vector<int> TIES;
+ for (int j = 0; j < ranks.size(); j++) {
+ if (m->control_pressed) { return W; }
+ rankTotal += (j+1);
+ ties.push_back(&(ranks[j]));
+
+ if (j != ranks.size()-1) { // you are not the last so you can look ahead
+ if (ranks[j].score != ranks[j+1].score) { // you are done with ties, rank them and continue
+ if (ties.size() > 1) { TIES.push_back(ties.size()); }
+ 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
+ if (ties.size() > 1) { TIES.push_back(ties.size()); }
+ for (int k = 0; k < ties.size(); k++) {
+ float thisrank = rankTotal / (float) ties.size();
+ (*ties[k]).score = thisrank;
+ }
+ }
+ }
+
+ //from R wilcox.test function
+ //STATISTIC <- sum(r[seq_along(x)]) - n.x * (n.x + 1)/2
+ double sumRanks = 0.0;
+ for (int i = 0; i < ranks.size(); i++) {
+ if (m->control_pressed) { return W; }
+ if (ranks[i].name == "x") { sumRanks += ranks[i].score; }
+ }
+
+ W = sumRanks - x.size() * ((double)(x.size() + 1)) / 2.0;
+
+ //exact <- (n.x < 50) && (n.y < 50)
+ bool findExact = false;
+ if ((x.size() < 50) && (y.size() < 50)) { findExact = true; }
+
+
+ if (findExact && (TIES.size() == 0)) { //find exact and no ties
+ //PVAL <- switch(alternative, two.sided = {
+ //p <- if (STATISTIC > (n.x * n.y/2))
+ PWilcox wilcox;
+ double pval = 0.0;
+ if (W > ((double)x.size()*y.size()/2.0)) {
+ //pwilcox(STATISTIC-1, n.x, n.y, lower.tail = FALSE)
+ pval = wilcox.pwilcox(W-1, x.size(), y.size(), false);
+ }else {
+ //pwilcox(STATISTIC,n.x, n.y)
+ pval = wilcox.pwilcox(W, x.size(), y.size(), true);
+ }
+ sig = 2.0 * pval;
+ if (1.0 < sig) { sig = 1.0; }
+ }else {
+ //z <- STATISTIC - n.x * n.y/2
+ double z = W - (double)(x.size() * y.size()/2.0);
+ //NTIES <- table(r)
+ double sum = 0.0;
+ for (int j = 0; j < TIES.size(); j++) { sum += ((TIES[j]*TIES[j]*TIES[j])-TIES[j]); }
+
+ //SIGMA <- sqrt((n.x * n.y/12) * ((n.x + n.y + 1) -
+ //sum(NTIES^3 - NTIES)/((n.x + n.y) * (n.x + n.y -
+ //1))))
+ double sigma = 0.0;
+ double firstTerm = (double)(x.size() * y.size()/12.0);
+ double secondTerm = (double)(x.size() + y.size() + 1) - sum / (double)((x.size() + y.size()) * (x.size() + y.size() - 1));
+ sigma = sqrt(firstTerm * secondTerm);
+
+ //CORRECTION <- switch(alternative, two.sided = sign(z) * 0.5, greater = 0.5, less = -0.5)
+ double CORRECTION = 0.0;
+ if (z < 0) { CORRECTION = -1.0; }
+ else if (z > 0) { CORRECTION = 1.0; }
+ CORRECTION *= 0.5;
+
+ z = (z - CORRECTION)/sigma;
+
+ //PVAL <- switch(alternative, two.sided = 2 * min(pnorm(z), pnorm(z, lower.tail = FALSE)))
+ sig = pnorm(z);
+ if ((1.0-sig) < sig) { sig = 1.0 - sig; }
+ sig *= 2;
+ }
+
+ return W;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "LinearAlgebra", "calcWilcoxon");
+ exit(1);
+ }
+}
+/*********************************************************************************************************************************/
+double LinearAlgebra::choose(double n, double k){
+ try {
+ n = floor(n + 0.5);
+ k = floor(k + 0.5);
+
+ double lchoose = gammln(n + 1.0) - gammln(k + 1.0) - gammln(n - k + 1.0);
+
+ return (floor(exp(lchoose) + 0.5));
+ }
+ catch(exception& e) {
+ m->errorOut(e, "LinearAlgebra", "choose");
+ exit(1);
+ }
+}
/*********************************************************************************************************************************/
double LinearAlgebra::calcSpearman(vector<double>& x, vector<double>& y, double& sig){
try {
}
}
-
/*********************************************************************************************************************************/
vector<vector<double> > LinearAlgebra::getInverse(vector<vector<double> > matrix){
exit(1);
}
}
-
-/*********************************************************************************************************************************/
projects / mothur.git / blobdiff