fixed classify.seqs output file name - had issue if reference taxonomy file did not...

[mothur.git] / cooccurrencecommand.cpp

diff --git a/cooccurrencecommand.cpp b/cooccurrencecommand.cpp
index 6363f2a0bafec54c478317a6b33b44b336f95b2a..e4c915d6a14e4a9755a17d64c073fdda1440803b 100644 (file)
--- a/cooccurrencecommand.cpp
+++ b/cooccurrencecommand.cpp
@@ -180,7 +180,7 @@ int CooccurrenceCommand::execute(){
         m->openOutputFile(outputFileName, out);
         outputNames.push_back(outputFileName);  outputTypes["summary"].push_back(outputFileName);
         out.setf(ios::fixed, ios::floatfield); out.setf(ios::showpoint);
-        out << "metric\tlabel\tScore\tzScore\n";
+        out << "metric\tlabel\tScore\tzScore\tstandardDeviation\n";
 
                //as long as you are not at the end of the file or done wih the lines you want
                while((lookup[0] != NULL) && ((allLines == 1) || (userLabels.size() != 0))) {
@@ -272,6 +272,12 @@ int CooccurrenceCommand::execute(){
 int CooccurrenceCommand::getCooccurrence(vector<SharedRAbundVector*>& thisLookUp, ofstream& out){
     try {
         int numOTUS = thisLookUp[0]->getNumBins();
+        
+        if(numOTUS < 2) {
+            m->mothurOut("Not enough OTUs for co-occurrence analysis, skipping"); m->mothurOutEndLine();
+            return 0;
+        }
+        
         vector< vector<int> > co_matrix; co_matrix.resize(thisLookUp[0]->getNumBins());
         for (int i = 0; i < thisLookUp[0]->getNumBins(); i++) { co_matrix[i].resize((thisLookUp.size()), 0); }
         vector<int> columntotal; columntotal.resize(thisLookUp.size(), 0);
@@ -543,7 +549,7 @@ int CooccurrenceCommand::getCooccurrence(vector<SharedRAbundVector*>& thisLookUp
         
         m->mothurOut("zscore: " + toString(zscore)); m->mothurOutEndLine();
         m->mothurOut("standard deviation: " + toString(sd)); m->mothurOutEndLine();
-        out << metric << '\t' << thisLookUp[0]->getLabel() << '\t' << nullMean << '\t' << zscore << endl;
+        out << metric << '\t' << thisLookUp[0]->getLabel() << '\t' << nullMean << '\t' << zscore << '\t' << sd << endl;
         
         return 0;
     }