test

fsl_glm.cc

@@ -24,9 +24,7 @@ using namespace std;
 
   string title=string("fsl_glm (Version 1.1)")+
 		string("\nCopyright(c) 2004-2009, University of Oxford (Christian F. Beckmann)\n")+
-		string(" \n Simple GLM usign ordinary least-squares (OLS) regression on\n")+
-		string(" time courses and/or 3D/4D imges against time courses \n")+
-		string(" or 3D/4D images");
+    string(" \n Simple GLM allowing temporal or spatial regression on either text data or images\n");
   string examples="fsl_glm -i <input> -d <design> -o <output> [options]";
 
 //Command line Options {
@@ -37,7 +35,7 @@ using namespace std;
 		string("output file name for GLM parameter estimates (GLM betas)"),
 		false, requires_argument);
   Option<string> fndesign(string("-d,--design"), string(""),
-		string("file name of the GLM design matrix (time courses or spatial maps)"),
+		string("file name of the GLM design matrix (text time courses for temporal regression or an image file for spatial regression )"),
 		false, requires_argument);
   Option<string> fnmask(string("-m,--mask"), string(""),
 		string("mask image file name if input is image"),
@@ -167,7 +165,7 @@ int setup(int &dof){
 			if(!samesize(tmpdata[0],mask)){
 				cerr << "ERROR: Mask image does not match input image" << endl;
 				return 1;
-			};
+ 			};
 		}else{
 			if(debug.value())
 				cout << "Creating mask image" << endl;
@@ -186,7 +184,7 @@ int setup(int &dof){
 		}
 	}
 	else
-		data = read_ascii_matrix(fnin.value());	
+	data = read_ascii_matrix(fnin.value());	
 
 	if(fsl_imageexists(fndesign.value())){//read design
 		if(debug.value())
@@ -205,8 +203,17 @@ int setup(int &dof){
 		design = read_ascii_matrix(fndesign.value());
 	}
 
-        dof=(int)ols_dof(design);
+	cerr << data << endl << design << endl;
+
+	if (perf_demean.value() ) {
+		if(debug.value())
+			cout << "De-meaning the data matrix" << endl;
+		data = remmean(data,1);
+	}
+
+	dof=ols_dof(design);
 	Matrix baseConfounds;
+
 	if ( textConfounds.set() ) {
 	  baseConfounds=read_ascii_matrix( textConfounds.value().at(0) );
 	  for(unsigned int i=1; i< textConfounds.value().size(); i++) 
@@ -238,14 +245,6 @@ int setup(int &dof){
 	  dof-=confounds.size();
 	}
 
-
-
-	if(perf_demean.value()){
-		if(debug.value())
-			cout << "De-meaning the data matrix" << endl;
-		data = remmean(data,1);
-		dof-=1;
-	}
 	if(normdat.value()){
 		if(debug.value())
 			cout << "Normalising data matrix to unit std-deviation" << endl;
@@ -253,11 +252,17 @@ int setup(int &dof){
 	}
 
 	meanR=mean(data,1);
+
 	if(perf_demean.value()){
 		if(debug.value())
 			cout << "De-meaning design matrix" << endl;	
 		design = remmean(design,1);
+		dof-=1;
 	}
+
+	cerr << data << endl << design << endl;
+
+
 	if(normdes.value()){
 		if(debug.value())
 			cout << "Normalising design matrix to unit std-deviation" << endl;