CommandParameter pgroups("groups", "String", "", "", "", "", "",false,false); parameters.push_back(pgroups);
CommandParameter pcalc("calc", "Multiple", "sharedsobs-sharedchao-sharedace-jabund-sorabund-jclass-sorclass-jest-sorest-thetayc-thetan-kstest-sharednseqs-ochiai-anderberg-kulczynski-kulczynskicody-lennon-morisitahorn-braycurtis-whittaker-odum-canberra-structeuclidean-structchord-hellinger-manhattan-structpearson-soergel-spearman-structkulczynski-speciesprofile-hamming-structchi2-gower-memchi2-memchord-memeuclidean-mempearson", "jclass-thetayc", "", "", "",true,false); parameters.push_back(pcalc);
CommandParameter poutput("output", "Multiple", "lt-square", "lt", "", "", "",false,false); parameters.push_back(poutput);
CommandParameter pgroups("groups", "String", "", "", "", "", "",false,false); parameters.push_back(pgroups);
CommandParameter pcalc("calc", "Multiple", "sharedsobs-sharedchao-sharedace-jabund-sorabund-jclass-sorclass-jest-sorest-thetayc-thetan-kstest-sharednseqs-ochiai-anderberg-kulczynski-kulczynskicody-lennon-morisitahorn-braycurtis-whittaker-odum-canberra-structeuclidean-structchord-hellinger-manhattan-structpearson-soergel-spearman-structkulczynski-speciesprofile-hamming-structchi2-gower-memchi2-memchord-memeuclidean-mempearson", "jclass-thetayc", "", "", "",true,false); parameters.push_back(pcalc);
CommandParameter poutput("output", "Multiple", "lt-square", "lt", "", "", "",false,false); parameters.push_back(poutput);
+ CommandParameter pmode("mode", "Multiple", "average-median", "average", "", "", "",false,false); parameters.push_back(pmode);
CommandParameter pprocessors("processors", "Number", "", "1", "", "", "",false,false); parameters.push_back(pprocessors);
CommandParameter piters("iters", "Number", "", "1000", "", "", "",false,false); parameters.push_back(piters);
CommandParameter pinputdir("inputdir", "String", "", "", "", "", "",false,false); parameters.push_back(pinputdir);
CommandParameter pprocessors("processors", "Number", "", "1", "", "", "",false,false); parameters.push_back(pprocessors);
CommandParameter piters("iters", "Number", "", "1000", "", "", "",false,false); parameters.push_back(piters);
CommandParameter pinputdir("inputdir", "String", "", "", "", "", "",false,false); parameters.push_back(pinputdir);
- helpString += "The dist.shared command parameters are shared, groups, calc, output, processors, subsample, iters and label. shared is a required, unless you have a valid current file.\n";
+ helpString += "The dist.shared command parameters are shared, groups, calc, output, processors, subsample, iters, mode, and label. shared is a required, unless you have a valid current file.\n";
helpString += "The groups parameter allows you to specify which of the groups in your groupfile you would like included used.\n";
helpString += "The group names are separated by dashes. The label parameter allows you to select what distance levels you would like distance matrices created for, and is also separated by dashes.\n";
helpString += "The iters parameter allows you to choose the number of times you would like to run the subsample.\n";
helpString += "The subsample parameter allows you to enter the size pergroup of the sample or you can set subsample=T and mothur will use the size of your smallest group.\n";
helpString += "The dist.shared command should be in the following format: dist.shared(groups=yourGroups, calc=yourCalcs, label=yourLabels).\n";
helpString += "The output parameter allows you to specify format of your distance matrix. Options are lt, and square. The default is lt.\n";
helpString += "The groups parameter allows you to specify which of the groups in your groupfile you would like included used.\n";
helpString += "The group names are separated by dashes. The label parameter allows you to select what distance levels you would like distance matrices created for, and is also separated by dashes.\n";
helpString += "The iters parameter allows you to choose the number of times you would like to run the subsample.\n";
helpString += "The subsample parameter allows you to enter the size pergroup of the sample or you can set subsample=T and mothur will use the size of your smallest group.\n";
helpString += "The dist.shared command should be in the following format: dist.shared(groups=yourGroups, calc=yourCalcs, label=yourLabels).\n";
helpString += "The output parameter allows you to specify format of your distance matrix. Options are lt, and square. The default is lt.\n";
helpString += "Example dist.shared(groups=A-B-C, calc=jabund-sorabund).\n";
helpString += "The default value for groups is all the groups in your groupfile.\n";
helpString += "The default value for calc is jclass and thetayc.\n";
helpString += "Example dist.shared(groups=A-B-C, calc=jabund-sorabund).\n";
helpString += "The default value for groups is all the groups in your groupfile.\n";
helpString += "The default value for calc is jclass and thetayc.\n";
+string MatrixOutputCommand::getOutputFileNameTag(string type, string inputName=""){
+ try {
+ string outputFileName = "";
+ map<string, vector<string> >::iterator it;
+
+ //is this a type this command creates
+ it = outputTypes.find(type);
+ if (it == outputTypes.end()) { m->mothurOut("[ERROR]: this command doesn't create a " + type + " output file.\n"); }
+ else {
+ if (type == "phylip") { outputFileName = "dist"; }
+ else { m->mothurOut("[ERROR]: No definition for type " + type + " output file tag.\n"); m->control_pressed = true; }
+ }
+ return outputFileName;
+ }
+ catch(exception& e) {
+ m->errorOut(e, "MatrixOutputCommand", "getOutputFileNameTag");
+ exit(1);
+ }
+}
+//**********************************************************************************************************************
output = validParameter.validFile(parameters, "output", false); if(output == "not found"){ output = "lt"; }
if ((output != "lt") && (output != "square")) { m->mothurOut(output + " is not a valid output form. Options are lt and square. I will use lt."); m->mothurOutEndLine(); output = "lt"; }
output = validParameter.validFile(parameters, "output", false); if(output == "not found"){ output = "lt"; }
if ((output != "lt") && (output != "square")) { m->mothurOut(output + " is not a valid output form. Options are lt and square. I will use lt."); m->mothurOutEndLine(); output = "lt"; }
groups = validParameter.validFile(parameters, "groups", false);
if (groups == "not found") { groups = ""; }
groups = validParameter.validFile(parameters, "groups", false);
if (groups == "not found") { groups = ""; }
out << lookup[b]->getGroup() << '\t';
for (int n = 0; n < simMatrix[b].size(); n++) {
out << simMatrix[b][n] << '\t';
out << lookup[b]->getGroup() << '\t';
for (int n = 0; n < simMatrix[b].size(); n++) {
out << simMatrix[b][n] << '\t';
+ for (int i = 0; i < calcDists.size(); i++) {
+ for (int j = 0; j < calcDists[i].size(); j++) {
+ if (m->debug) { m->mothurOut("[DEBUG]: Results: iter = " + toString(thisIter) + ", " + thisLookup[calcDists[i][j].seq1]->getGroup() + " - " + thisLookup[calcDists[i][j].seq2]->getGroup() + " distance = " + toString(calcDists[i][j].dist) + ".\n"); }
+ }
+ }
}else { //print results for whole dataset
for (int i = 0; i < calcDists.size(); i++) {
if (m->control_pressed) { break; }
}else { //print results for whole dataset
for (int i = 0; i < calcDists.size(); i++) {
if (m->control_pressed) { break; }
ofstream outDist;
m->openOutputFile(distFileName, outDist);
outDist.setf(ios::fixed, ios::floatfield); outDist.setf(ios::showpoint);
ofstream outDist;
m->openOutputFile(distFileName, outDist);
outDist.setf(ios::fixed, ios::floatfield); outDist.setf(ios::showpoint);
//we need to find the average distance and standard deviation for each groups distance
vector< vector<seqDist> > calcAverages; calcAverages.resize(matrixCalculators.size());
//we need to find the average distance and standard deviation for each groups distance
vector< vector<seqDist> > calcAverages; calcAverages.resize(matrixCalculators.size());
calcAverages[i].resize(calcDistsTotals[0][i].size());
for (int j = 0; j < calcAverages[i].size(); j++) {
calcAverages[i].resize(calcDistsTotals[0][i].size());
for (int j = 0; j < calcAverages[i].size(); j++) {
- calcAverages[i][j].seq1 = calcDists[i][j].seq1;
- calcAverages[i][j].seq2 = calcDists[i][j].seq2;
+ calcAverages[i][j].seq1 = calcDistsTotals[0][i][j].seq1;
+ calcAverages[i][j].seq2 = calcDistsTotals[0][i][j].seq2;
-
- for (int thisIter = 0; thisIter < iters; thisIter++) { //sum all groups dists for each calculator
- for (int i = 0; i < calcAverages.size(); i++) { //initialize sums to zero.
+ if (mode == "average") {
+ for (int thisIter = 0; thisIter < iters; thisIter++) { //sum all groups dists for each calculator
+ for (int i = 0; i < calcAverages.size(); i++) { //initialize sums to zero.
+ for (int j = 0; j < calcAverages[i].size(); j++) {
+ calcAverages[i][j].dist += calcDistsTotals[thisIter][i][j].dist;
+ if (m->debug) { m->mothurOut("[DEBUG]: Totaling for average calc: iter = " + toString(thisIter) + ", " + thisLookup[calcDistsTotals[thisIter][i][j].seq1]->getGroup() + " - " + thisLookup[calcDistsTotals[thisIter][i][j].seq2]->getGroup() + " distance = " + toString(calcDistsTotals[thisIter][i][j].dist) + ". New total = " + toString(calcAverages[i][j].dist) + ".\n"); }
+ }
+ }
+ }
+
+ for (int i = 0; i < calcAverages.size(); i++) { //finds average.
- }
-
- for (int i = 0; i < calcAverages.size(); i++) { //finds average.
- for (int j = 0; j < calcAverages[i].size(); j++) {
- calcAverages[i][j].dist /= (float) iters;
+ }else { //find median
+ for (int i = 0; i < calcAverages.size(); i++) { //for each calc
+ for (int j = 0; j < calcAverages[i].size(); j++) { //for each comparison
+ vector<double> dists;
+ for (int thisIter = 0; thisIter < iters; thisIter++) { //for each subsample
+ dists.push_back(calcDistsTotals[thisIter][i][j].dist);
+ }
+ sort(dists.begin(), dists.end());
+ calcAverages[i][j].dist = dists[(iters/2)];
+ }
//find standard deviation
vector< vector<seqDist> > stdDev; stdDev.resize(matrixCalculators.size());
for (int i = 0; i < stdDev.size(); i++) { //initialize sums to zero.
stdDev[i].resize(calcDistsTotals[0][i].size());
for (int j = 0; j < stdDev[i].size(); j++) {
//find standard deviation
vector< vector<seqDist> > stdDev; stdDev.resize(matrixCalculators.size());
for (int i = 0; i < stdDev.size(); i++) { //initialize sums to zero.
stdDev[i].resize(calcDistsTotals[0][i].size());
for (int j = 0; j < stdDev[i].size(); j++) {
outputNames.push_back(distFileName); outputTypes["phylip"].push_back(distFileName);
ofstream outAve;
m->openOutputFile(distFileName, outAve);
outputNames.push_back(distFileName); outputTypes["phylip"].push_back(distFileName);
ofstream outAve;
m->openOutputFile(distFileName, outAve);
outputNames.push_back(distFileName); outputTypes["phylip"].push_back(distFileName);
ofstream outSTD;
m->openOutputFile(distFileName, outSTD);
outputNames.push_back(distFileName); outputTypes["phylip"].push_back(distFileName);
ofstream outSTD;
m->openOutputFile(distFileName, outSTD);
int MatrixOutputCommand::driver(vector<SharedRAbundVector*> thisLookup, int start, int end, vector< vector<seqDist> >& calcDists) {
try {
vector<SharedRAbundVector*> subset;
int MatrixOutputCommand::driver(vector<SharedRAbundVector*> thisLookup, int start, int end, vector< vector<seqDist> >& calcDists) {
try {
vector<SharedRAbundVector*> subset;
for (int k = start; k < end; k++) { // pass cdd each set of groups to compare
for (int l = 0; l < k; l++) {
for (int k = start; k < end; k++) { // pass cdd each set of groups to compare
for (int l = 0; l < k; l++) {