comparison logistic_regression_vif.py @ 0:ffcdde989859 draft

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0:ffcdde989859

#!/usr/bin/env python

#from galaxy import eggs
import sys, string
#from rpy import *
import rpy2.robjects as robjects
import rpy2.rlike.container as rlc
import rpy2.rinterface as ri
r = robjects.r
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 = []
x_vector = []
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)
                x_vector.append(xval)
        except Exception, e:
            print e
            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")
#    #r('library(car)')
#    #r.assign('dat',dat)
#    #r.assign('ncols',len(x_cols))
#    #r.vif(r('glm(dat$y ~ ., data = na.exclude(data.frame(as.matrix(dat$x,ncol=ncols))->datx),family="binomial")')).as_py()
#   
#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.")

fv = robjects.FloatVector(x_vector)
m = r['matrix'](fv, ncol=len(x_cols),byrow=True)
# ensure order for generating formula
od = rlc.OrdDict([('y',robjects.FloatVector(y_vals)),('x',m)])
dat = robjects.DataFrame(od)
# convert dat.names: ["y","x.1","x.2"] to formula string: 'y ~ x.1 + x.2'
formula = ' + '.join(dat.names).replace('+','~',1)
print formula
try:
    linear_model = r.glm(formula,  data =  r['na.exclude'](dat), family="binomial")
except Exception, rex:
    stop_err("Error performing linear 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('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")'))
        od2 = rlc.OrdDict([('datx', m)])
        glm_data_frame = robjects.DataFrame(od2)
        glm_result = r.glm("dat$y ~ .", data = r['na.exclude'](glm_data_frame),family="binomial")
        print 'Have glm'
        vif = r.vif(glm_result)
    except Exception, rex:        
        print rex
else:
    novif=1
    
#set_default_mode(BASIC_CONVERSION)

#coeffs=linear_model.as_py()['coefficients']
coeffs=linear_model.rx2('coefficients')
#null_deviance=linear_model.as_py()['null.deviance']
null_deviance=linear_model.rx2('null.deviance')[0]
#residual_deviance=linear_model.as_py()['deviance']
residual_deviance=linear_model.rx2('deviance')[0]
#yintercept= coeffs['(Intercept)']
yintercept= coeffs.rx2('(Intercept)')[0]

summary = r.summary(linear_model)
#co = summary.get('coefficients', 'NA')
co = summary.rx2("coefficients")
print co
"""
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)[0]
    #pvaly = r.round(float(co[0][3]), digits=10)
    pvaly = r.round(float(co.rx(1,4)[0]), digits=10)[0]
except Exception, e:
    print str(e)
    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)

print coeffs
if len(x_vals) == 1:    #Simple linear  regression case with 1 predictor variable
    try:
        #slope = r.round(float(coeffs['x']), digits=10)
        raw_slope = coeffs.rx2('x')[0]
        slope = r.round(float(raw_slope), digits=10)[0] 
    except:
        slope = 'NA'
    try:
        #pval = r.round(float(co[1][3]), digits=10)
        pval = r.round(float(co.rx2(2,4)[0]), digits=10)[0]
    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()):
    print len(coeffs.names)
    while ind < len(coeffs.names):
        try:
            #slope = r.round(float(coeffs['x'+str(ind)]), digits=10)
            raw_slope = coeffs.rx2('x.' + str(ind))[0]
            slope = r.round(float(raw_slope), digits=10)[0]
        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)
            pval = r.round(float(co.rx2(ind+1, 4)[0]), digits=10)[0]
        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')
rsq = summary.rx2('r.squared')
if rsq == ri.RNULLType():
    rsq = 'NA'
else:
    rsq = rsq[0]


try:
    #rsq= r.round(float((null_deviance-residual_deviance)/null_deviance), digits=5)
    rsq= r.round(float((null_deviance-residual_deviance)/null_deviance), digits=5)[0]
    #null_deviance= r.round(float(null_deviance), digits=5)
    null_deviance= r.round(float(null_deviance), digits=5)[0]
    #residual_deviance= r.round(float(residual_deviance), digits=5)
    residual_deviance= r.round(float(residual_deviance), digits=5)[0]
    
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(vif.names):
        print >>fout,'c'+str(x_cols[count]+1) ,str(vif.rx2(i)[0])
        count+=1
elif novif==1:
    print >>fout, "vif can calculate only when model have more than 1 predictor"