# HG changeset patch
# User devteam
# Date 1396357979 14400
# Node ID 5cbb241afdc5af37f0cdfde009344e49dbece07d

Imported from capsule None

diff -r 000000000000 -r 5cbb241afdc5 linear_regression.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/linear_regression.py	Tue Apr 01 09:12:59 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 5cbb241afdc5 linear_regression.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/linear_regression.xml	Tue Apr 01 09:12:59 2014 -0400
@@ -0,0 +1,76 @@
+<tool id="LinearRegression1" name="Perform Linear Regression" version="1.0.1">
+  <description> </description>
+  <requirements>
+    <requirement type="package" version="2.11.0">R</requirement>
+    <requirement type="package" version="1.7.1">numpy</requirement>
+    <requirement type="package" version="1.0.3">rpy</requirement>
+  </requirements>
+  <command interpreter="python">
+    linear_regression.py 
+      $input1
+      $response_col
+      $predictor_cols
+      $out_file1
+      $out_file2
+      1>/dev/null
+  </command>
+  <inputs>
+    <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/>
+    <param name="response_col" label="Response column (Y)" type="data_column" data_ref="input1" numerical="True"/>
+    <param name="predictor_cols" label="Predictor columns (X)" type="data_column" data_ref="input1" numerical="True" multiple="true" >
+        <validator type="no_options" message="Please select at least one column."/>
+    </param>
+  </inputs>
+  <outputs>
+    <data format="input" name="out_file1" metadata_source="input1" />
+    <data format="pdf" name="out_file2" />
+  </outputs>
+  <requirements>
+    <requirement type="python-module">rpy</requirement>
+  </requirements>
+  <tests>
+    <test>
+        <param name="input1" value="regr_inp.tabular"/>
+        <param name="response_col" value="3"/>
+        <param name="predictor_cols" value="1,2"/>
+        <output name="out_file1" file="regr_out.tabular"/>
+        <output name="out_file2" file="regr_out.pdf"/>
+    </test>
+  </tests>
+  <help>
+
+
+.. class:: infomark
+
+**TIP:** If your data is not TAB delimited, use *Edit Datasets-&gt;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.
+
+
+  </help>
+</tool>
diff -r 000000000000 -r 5cbb241afdc5 test-data/regr_inp.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regr_inp.tabular	Tue Apr 01 09:12:59 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 5cbb241afdc5 test-data/regr_out.pdf
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regr_out.pdf	Tue Apr 01 09:12:59 2014 -0400
@@ -0,0 +1,1590 @@
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diff -r 000000000000 -r 5cbb241afdc5 test-data/regr_out.tabular
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regr_out.tabular	Tue Apr 01 09:12:59 2014 -0400
@@ -0,0 +1,10 @@
+Y-intercept	-28.1169126094
+p-value (Y-intercept)	0.2942632048
+Slope (c1)	5.7471341245
+p-value (c1)	0.001732877
+Slope (c2)	0.9042745374
+p-value (c2)	0.1146675468
+R-squared	0.93881
+Adjusted R-squared	0.92132
+F-statistic	{'dendf': 7.0, 'value': '53.69463', 'numdf': 2.0}
+Sigma	7.4345007113
diff -r 000000000000 -r 5cbb241afdc5 tool_dependencies.xml
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/tool_dependencies.xml	Tue Apr 01 09:12:59 2014 -0400
@@ -0,0 +1,12 @@
+<?xml version="1.0"?>
+<tool_dependency>
+  <package name="numpy" version="1.7.1">
+      <repository changeset_revision="55a7a5e9d63f" name="package_numpy_1_7" owner="devteam" prior_installation_required="False" toolshed="http://testtoolshed.g2.bx.psu.edu" />
+    </package>
+    <package name="rpy" version="1.0.3">
+      <repository changeset_revision="c0eb80864491" name="package_rpy_1_0_3" owner="devteam" prior_installation_required="False" toolshed="http://testtoolshed.g2.bx.psu.edu" />
+    </package>
+    <package name="R" version="2.11.0">
+      <repository changeset_revision="2c0a13200a73" name="package_r_2_11_0" owner="devteam" prior_installation_required="False" toolshed="http://testtoolshed.g2.bx.psu.edu" />
+    </package>
+</tool_dependency>