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1 <tool id="BestSubsetsRegression1" name="Perform Best-subsets Regression" version="0.0.1">
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2 <description> </description>
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3 <command interpreter="python">
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4 best_regression_subsets.py
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5 $input1
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6 $response_col
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7 $predictor_cols
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8 $out_file1
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9 $out_file2
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10 1>/dev/null
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11 2>/dev/null
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12 </command>
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13 <inputs>
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14 <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/>
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15 <param name="response_col" label="Response column (Y)" type="data_column" data_ref="input1" />
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16 <param name="predictor_cols" label="Predictor columns (X)" type="data_column" data_ref="input1" multiple="true" >
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17 <validator type="no_options" message="Please select at least one column."/>
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18 </param>
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19 </inputs>
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20 <outputs>
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21 <data format="input" name="out_file1" metadata_source="input1" />
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22 <data format="pdf" name="out_file2" />
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23 </outputs>
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24 <requirements>
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25 <requirement type="python-module">rpy</requirement>
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26 </requirements>
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27 <tests>
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28 <!-- Testing this tool will not be possible because this tool produces a pdf output file.
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29 -->
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30 </tests>
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31 <help>
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32
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33 .. class:: infomark
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34
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35 **TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters*
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36
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37 -----
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38
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39 .. class:: infomark
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40
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41 **What it does**
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42
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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.
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44
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45 -----
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46
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47 .. class:: warningmark
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48
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49 **Note**
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50
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51 - This tool currently treats all predictor and response variables as continuous variables.
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52
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53 - Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
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54
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55 - The 6 columns in the output are described below:
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56
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57 - Column 1 (Vars): denotes the number of variables in the model
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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.
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59 - Column 3 (R-sq): the fraction of variance explained by the model
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60 - Column 4 (Adj. R-sq): the above R-squared statistic adjusted, penalizing for higher number of predictors (p)
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61 - Column 5 (Cp): Mallow's Cp statistics
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62 - Column 6 (bic): Bayesian Information Criterion.
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63
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64
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65 </help>
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66 </tool>
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