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comparison partialR_square.xml @ 0:ffcdde989859 draft

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author iuc
date Tue, 29 Jul 2014 06:30:45 -0400
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1 <tool id="partialRsq" name="Compute partial R square" version="1.1.0">
2 <description> </description>
3 <expand macro="requirements" />
4 <macros>
5 <import>statistic_tools_macros.xml</import>
6 </macros>
7 <command interpreter="python">
8 partialR_square.py
9 $input1
10 $response_col
11 $predictor_cols
12 $out_file1
13 1>/dev/null
14 </command>
15 <inputs>
16 <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/>
17 <param name="response_col" label="Response column (Y)" type="data_column" data_ref="input1" />
18 <param name="predictor_cols" label="Predictor columns (X)" type="data_column" data_ref="input1" multiple="true">
19 <validator type="no_options" message="Please select at least one column."/>
20 </param>
21 </inputs>
22 <outputs>
23 <data format="input" name="out_file1" metadata_source="input1" />
24 </outputs>
25 <tests>
26 <!-- Test data with vlid values -->
27 <test>
28 <param name="input1" value="regr_inp.tabular"/>
29 <param name="response_col" value="3"/>
30 <param name="predictor_cols" value="1,2"/>
31 <output name="out_file1" file="partialR_result.tabular"/>
32 </test>
33
34 </tests>
35 <help>
36
37 .. class:: infomark
38
39 **TIP:** If your data is not TAB delimited, use *Edit Datasets-&gt;Convert characters*
40
41 -----
42
43 .. class:: infomark
44
45 **What it does**
46
47 This tool computes the Partial R squared for all possible variable subsets using the following formula:
48
49 **Partial R squared = [SSE(without i: 1,2,...,p-1) - SSE (full: 1,2,..,i..,p-1) / SSE(without i: 1,2,...,p-1)]**, which denotes the case where the 'i'th predictor is dropped.
50
51
52
53 In general, **Partial R squared = [SSE(without i: 1,2,...,p-1) - SSE (full: 1,2,..,i..,p-1) / SSE(without i: 1,2,...,p-1)]**, where,
54
55 - SSE (full: 1,2,..,i..,p-1) = Sum of Squares left out by the full set of predictors SSE(X1, X2 … Xp)
56 - SSE (full: 1,2,..,i..,p-1) = Sum of Squares left out by the set of predictors excluding; for example, if we omit the first predictor, it will be SSE(X2 … Xp).
57
58
59 The 4 columns in the output are described below:
60
61 - Column 1 (Model): denotes the variables present in the model
62 - Column 2 (R-sq): denotes the R-squared value corresponding to the model in Column 1
63 - Column 3 (Partial R squared_Terms): denotes the variable/s for which Partial R squared is computed. These are the variables that are absent in the reduced model in Column 1. A '-' in this column indicates that the model in Column 1 is the Full model.
64 - Column 4 (Partial R squared): denotes the Partial R squared value corresponding to the variable/s in Column 3. A '-' in this column indicates that the model in Column 1 is the Full model.
65
66 *R Development Core Team (2010). 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.*
67
68 </help>
69 </tool>