--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/logistic_regression_vif.py	Tue Apr 01 10:51:26 2014 -0400
@@ -0,0 +1,167 @@
+#!/usr/bin/env python
+
+import sys
+from rpy import *
+import numpy
+
+def stop_err(msg):
+    sys.stderr.write(msg)
+    sys.exit()
+
+infile = sys.argv[1]
+y_col = int(sys.argv[2])-1
+x_cols = sys.argv[3].split(',')
+outfile = sys.argv[4]
+
+
+print "Predictor columns: %s; Response column: %d" % ( x_cols, y_col+1 )
+fout = open(outfile,'w')
+elems = []
+for i, line in enumerate( file( infile ) ):
+    line = line.rstrip('\r\n')
+    if len( line )>0 and not line.startswith( '#' ):
+        elems = line.split( '\t' )
+        break
+    if i == 30:
+        break # Hopefully we'll never get here...
+
+if len( elems )<1:
+    stop_err( "The data in your input dataset is either missing or not formatted properly." )
+
+y_vals = []
+x_vals = []
+
+for k, col in enumerate(x_cols):
+    x_cols[k] = int(col)-1
+    x_vals.append([])
+
+NA = 'NA'
+for ind, line in enumerate( file( infile )):
+    if line and not line.startswith( '#' ):
+        try:
+            fields = line.split("\t")
+            try:
+                yval = float(fields[y_col])
+            except:
+                yval = r('NA')
+            y_vals.append(yval)
+            for k, col in enumerate(x_cols):
+                try:
+                    xval = float(fields[col])
+                except:
+                    xval = r('NA')
+                x_vals[k].append(xval)
+        except:
+            pass
+
+x_vals1 = numpy.asarray(x_vals).transpose()
+
+check1 = 0
+check0 = 0
+for i in y_vals:
+    if i == 1:
+        check1 = 1
+    if i == 0:
+        check0 = 1
+if check1 == 0 or check0 == 0:
+    sys.exit("Warning: logistic regression must have at least two classes")
+
+for i in y_vals:
+    if i not in [1, 0, r('NA')]:
+        print >> fout, str(i)
+        sys.exit("Warning: the current version of this tool can run only with two classes and need to be labeled as 0 and 1.")
+
+dat = r.list(x=array(x_vals1), y=y_vals)
+novif = 0
+set_default_mode(NO_CONVERSION)
+try:
+    linear_model = r.glm(r("y ~ x"), data=r.na_exclude(dat), family="binomial")
+except RException, rex:
+    stop_err("Error performing logistic regression on the input data.\nEither the response column or one of the predictor columns contain only non-numeric or invalid values.")
+if len(x_cols)>1:
+    try:
+        r('suppressPackageStartupMessages(library(car))')
+        r.assign('dat', dat)
+        r.assign('ncols', len(x_cols))
+        vif = r.vif(r('glm(dat$y ~ ., data = na.exclude(data.frame(as.matrix(dat$x,ncol=ncols))->datx), family="binomial")'))
+    except RException, rex:
+        print rex
+else:
+    novif = 1
+
+set_default_mode(BASIC_CONVERSION)
+
+coeffs = linear_model.as_py()['coefficients']
+null_deviance = linear_model.as_py()['null.deviance']
+residual_deviance = linear_model.as_py()['deviance']
+yintercept = coeffs['(Intercept)']
+summary = r.summary(linear_model)
+co = summary.get('coefficients', 'NA')
+"""
+if len(co) != len(x_vals)+1:
+    stop_err("Stopped performing logistic regression on the input data, since one of the predictor columns contains only non-numeric or invalid values.")
+"""
+
+try:
+    yintercept = r.round(float(yintercept), digits=10)
+    pvaly = r.round(float(co[0][3]), digits=10)
+except:
+    pass
+print >> fout, "response column\tc%d" % (y_col+1)
+tempP = []
+for i in x_cols:
+    tempP.append('c'+str(i+1))
+tempP = ','.join(tempP)
+print >> fout, "predictor column(s)\t%s" % (tempP)
+print >> fout, "Y-intercept\t%s" % (yintercept)
+print >> fout, "p-value (Y-intercept)\t%s" % (pvaly)
+
+if len(x_vals) == 1:    #Simple linear  regression case with 1 predictor variable
+    try:
+        slope = r.round(float(coeffs['x']), digits=10)
+    except:
+        slope = 'NA'
+    try:
+        pval = r.round(float(co[1][3]), digits=10)
+    except:
+        pval = 'NA'
+    print >> fout, "Slope (c%d)\t%s" % ( x_cols[0]+1, slope )
+    print >> fout, "p-value (c%d)\t%s" % ( x_cols[0]+1, pval )
+else:    #Multiple regression case with >1 predictors
+    ind = 1
+    while ind < len(coeffs.keys()):
+        try:
+            slope = r.round(float(coeffs['x'+str(ind)]), digits=10)
+        except:
+            slope = 'NA'
+        print >> fout, "Slope (c%d)\t%s" % ( x_cols[ind-1]+1, slope )
+        try:
+            pval = r.round(float(co[ind][3]), digits=10)
+        except:
+            pval = 'NA'
+        print >> fout, "p-value (c%d)\t%s" % ( x_cols[ind-1]+1, pval )
+        ind += 1
+
+rsq = summary.get('r.squared','NA')
+
+try:
+    rsq = r.round(float((null_deviance-residual_deviance)/null_deviance), digits=5)
+    null_deviance = r.round(float(null_deviance), digits=5)
+    residual_deviance = r.round(float(residual_deviance), digits=5)
+except:
+    pass
+
+print >> fout, "Null deviance\t%s" % (null_deviance)
+print >> fout, "Residual deviance\t%s" % (residual_deviance)
+print >> fout, "pseudo R-squared\t%s" % (rsq)
+print >> fout, "\n"
+print >> fout, 'vif'
+
+if novif == 0:
+    py_vif = vif.as_py()
+    count = 0
+    for i in sorted(py_vif.keys()):
+        print >> fout, 'c'+str(x_cols[count]+1), str(py_vif[i])
+        count += 1
+elif novif == 1:
+    print >> fout, "vif can calculate only when model have more than 1 predictor"