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1 <tool id="partialRsq" name="Compute partial R square" version="1.1.0">
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2 <description> </description>
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3 <expand macro="requirements" />
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4 <macros>
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5 <import>statistic_tools_macros.xml</import>
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6 </macros>
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7 <command interpreter="python">
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8 <![CDATA[
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9 partialR_square.py
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10 $input1
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11 $response_col
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12 $predictor_cols
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13 $out_file1
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14 1>/dev/null
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15 ]]>
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16 </command>
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17 <inputs>
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18 <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/>
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19 <param name="response_col" label="Response column (Y)" type="data_column" data_ref="input1" />
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20 <param name="predictor_cols" label="Predictor columns (X)" type="data_column" data_ref="input1" multiple="true">
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21 <validator type="no_options" message="Please select at least one column."/>
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22 </param>
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23 </inputs>
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24 <outputs>
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25 <data format="input" name="out_file1" metadata_source="input1" />
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26 </outputs>
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27 <tests>
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28 <!-- Test data with vlid values -->
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29 <test>
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30 <param name="input1" value="regr_inp.tabular"/>
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31 <param name="response_col" value="3"/>
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32 <param name="predictor_cols" value="1,2"/>
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33 <output name="out_file1" file="partialR_result.tabular"/>
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34 </test>
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35
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36 </tests>
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37 <help>
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38 <![CDATA[
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39
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40 .. class:: infomark
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41
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42 **TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters*
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43
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44 -----
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45
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46 .. class:: infomark
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47
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48 **What it does**
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49
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50 This tool computes the Partial R squared for all possible variable subsets using the following formula:
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51
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52 **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.
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53
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54
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55
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56 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,
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57
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58 - SSE (full: 1,2,..,i..,p-1) = Sum of Squares left out by the full set of predictors SSE(X1, X2 … Xp)
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59 - 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).
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60
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61
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62 The 4 columns in the output are described below:
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63
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64 - Column 1 (Model): denotes the variables present in the model
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65 - Column 2 (R-sq): denotes the R-squared value corresponding to the model in Column 1
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66 - 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.
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67 - 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.
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68
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69 *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.*
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70
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71 ]]>
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72 </help>
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73 </tool>
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