# HG changeset patch
# User devteam
# Date 1396363937 14400
# Node ID cf431604ec3e85dc4bf71b2e0018c537a6b4349a
Imported from capsule None
diff -r 000000000000 -r cf431604ec3e linear_regression.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/linear_regression.py Tue Apr 01 10:52:17 2014 -0400
@@ -0,0 +1,146 @@
+#!/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]
+outfile2 = sys.argv[5]
+
+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()
+
+dat = r.list(x=array(x_vals1), y=y_vals)
+
+set_default_mode(NO_CONVERSION)
+try:
+ linear_model = r.lm(r("y ~ x"), data = r.na_exclude(dat))
+except RException, 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.")
+set_default_mode(BASIC_CONVERSION)
+
+coeffs = linear_model.as_py()['coefficients']
+yintercept = coeffs['(Intercept)']
+summary = r.summary(linear_model)
+
+co = summary.get('coefficients', 'NA')
+"""
+if len(co) != len(x_vals)+1:
+ stop_err("Stopped performing linear 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, "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')
+adjrsq = summary.get('adj.r.squared','NA')
+fstat = summary.get('fstatistic','NA')
+sigma = summary.get('sigma','NA')
+
+try:
+ rsq = r.round(float(rsq), digits=5)
+ adjrsq = r.round(float(adjrsq), digits=5)
+ fval = r.round(fstat['value'], digits=5)
+ fstat['value'] = str(fval)
+ sigma = r.round(float(sigma), digits=10)
+except:
+ pass
+
+print >> fout, "R-squared\t%s" % (rsq)
+print >> fout, "Adjusted R-squared\t%s" % (adjrsq)
+print >> fout, "F-statistic\t%s" % (fstat)
+print >> fout, "Sigma\t%s" % (sigma)
+
+r.pdf( outfile2, 8, 8 )
+if len(x_vals) == 1: #Simple linear regression case with 1 predictor variable
+ sub_title = "Slope = %s; Y-int = %s" % ( slope, yintercept )
+ try:
+ r.plot(x=x_vals[0], y=y_vals, xlab="X", ylab="Y", sub=sub_title, main="Scatterplot with regression")
+ r.abline(a=yintercept, b=slope, col="red")
+ except:
+ pass
+else:
+ r.pairs(dat, main="Scatterplot Matrix", col="blue")
+try:
+ r.plot(linear_model)
+except:
+ pass
+r.dev_off()
diff -r 000000000000 -r cf431604ec3e linear_regression.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/linear_regression.xml Tue Apr 01 10:52:17 2014 -0400
@@ -0,0 +1,76 @@
+
+
+
+ R
+ numpy
+ rpy
+
+
+ linear_regression.py
+ $input1
+ $response_col
+ $predictor_cols
+ $out_file1
+ $out_file2
+ 1>/dev/null
+
+
+
+
+
+
+
+
+
+
+
+
+
+ rpy
+
+
+
+
+
+
+
+
+
+
+
+
+
+.. class:: infomark
+
+**TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters*
+
+-----
+
+.. class:: infomark
+
+**What it does**
+
+This tool uses the 'lm' function from R statistical package to perform linear regression on the input data. It outputs two files, one containing the summary statistics of the performed regression, and the other containing diagnostic plots to check whether model assumptions are satisfied.
+
+*R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.*
+
+-----
+
+.. class:: warningmark
+
+**Note**
+
+- This tool currently treats all predictor and response variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results.
+
+- Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
+
+- The summary statistics in the output are described below:
+
+ - sigma: the square root of the estimated variance of the random error (standard error of the residiuals)
+ - R-squared: the fraction of variance explained by the model
+ - Adjusted R-squared: the above R-squared statistic adjusted, penalizing for the number of the predictors (p)
+ - p-value: p-value for the t-test of the null hypothesis that the corresponding slope is equal to zero against the two-sided alternative.
+
+
+
+
diff -r 000000000000 -r cf431604ec3e test-data/regr_inp.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regr_inp.tabular Tue Apr 01 10:52:17 2014 -0400
@@ -0,0 +1,10 @@
+7 33 42
+4 41 33
+16 7 75
+3 49 28
+21 5 91
+8 31 55
+7 35 52
+5 30 16
+15 10 69
+20 10 94
diff -r 000000000000 -r cf431604ec3e test-data/regr_out.pdf
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regr_out.pdf Tue Apr 01 10:52:17 2014 -0400
@@ -0,0 +1,1590 @@
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diff -r 000000000000 -r cf431604ec3e test-data/regr_out.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regr_out.tabular Tue Apr 01 10:52:17 2014 -0400
@@ -0,0 +1,10 @@
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+F-statistic {'dendf': 7.0, 'value': '53.69463', 'numdf': 2.0}
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diff -r 000000000000 -r cf431604ec3e tool_dependencies.xml
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
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