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comparison best_regression_subsets.xml @ 0:54c7a01a2cc7

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Imported from capsule None
author devteam
date Tue, 01 Apr 2014 10:50:17 -0400
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1 <tool id="BestSubsetsRegression1" name="Perform Best-subsets Regression" version="0.0.1">
2 <description> </description>
3 <command interpreter="python">
4 best_regression_subsets.py
5 $input1
6 $response_col
7 $predictor_cols
8 $out_file1
9 $out_file2
10 1>/dev/null
11 2>/dev/null
12 </command>
13 <inputs>
14 <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/>
15 <param name="response_col" label="Response column (Y)" type="data_column" data_ref="input1" />
16 <param name="predictor_cols" label="Predictor columns (X)" type="data_column" data_ref="input1" multiple="true" >
17 <validator type="no_options" message="Please select at least one column."/>
18 </param>
19 </inputs>
20 <outputs>
21 <data format="input" name="out_file1" metadata_source="input1" />
22 <data format="pdf" name="out_file2" />
23 </outputs>
24 <requirements>
25 <requirement type="python-module">rpy</requirement>
26 </requirements>
27 <tests>
28 <!-- Testing this tool will not be possible because this tool produces a pdf output file.
29 -->
30 </tests>
31 <help>
32
33 .. class:: infomark
34
35 **TIP:** If your data is not TAB delimited, use *Edit Datasets-&gt;Convert characters*
36
37 -----
38
39 .. class:: infomark
40
41 **What it does**
42
43 This tool uses the 'regsubsets' function from R statistical package for regression subset selection. It outputs two files, one containing a table with the best subsets and the corresponding summary statistics, and the other containing the graphical representation of the results.
44
45 -----
46
47 .. class:: warningmark
48
49 **Note**
50
51 - This tool currently treats all predictor and response variables as continuous variables.
52
53 - Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
54
55 - The 6 columns in the output are described below:
56
57 - Column 1 (Vars): denotes the number of variables in the model
58 - Column 2 ([c2 c3 c4...]): represents a list of the user-selected predictor variables (full model). An asterix denotes the presence of the corresponding predictor variable in the selected model.
59 - Column 3 (R-sq): the fraction of variance explained by the model
60 - Column 4 (Adj. R-sq): the above R-squared statistic adjusted, penalizing for higher number of predictors (p)
61 - Column 5 (Cp): Mallow's Cp statistics
62 - Column 6 (bic): Bayesian Information Criterion.
63
64
65 </help>
66 </tool>