<tool id="BestSubsetsRegression1" name="Perform Best-subsets Regression">
  <description> </description>
  <command interpreter="python">
    best_regression_subsets.py 
      $input1
      $response_col
      $predictor_cols
      $out_file1
      $out_file2
      1>/dev/null
      2>/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" />
    <param name="predictor_cols" label="Predictor columns (X)" type="data_column" data_ref="input1" 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>
    <!-- Testing this tool will not be possible because this tool produces a pdf output file.
    -->
  </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 '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.  

-----

.. class:: warningmark

**Note**

- This tool currently treats all predictor and response variables as continuous variables. 

- Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.

- The 6 columns in the output are described below:

  - Column 1 (Vars): denotes the number of variables in the model
  - 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.
  - Column 3 (R-sq): the fraction of variance explained by the model
  - Column 4 (Adj. R-sq): the above R-squared statistic adjusted, penalizing for higher number of predictors (p)
  - Column 5 (Cp): Mallow's Cp statistics  
  - Column 6 (bic): Bayesian Information Criterion. 


  </help>
</tool>