view partialR_square.xml @ 90:b061185bcb83 draft

Uploaded

<tool id="partialRsq" name="Compute partial R square" version="1.1.0">
  <description> </description>
  <expand macro="requirements" />
    <macros>
        <import>statistic_tools_macros.xml</import>
    </macros>
  <command interpreter="python">
    partialR_square.py 
      $input1
      $response_col
      $predictor_cols
      $out_file1
      1>/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" />
  </outputs>
  <tests>
    <!-- Test data with vlid values -->
  	<test>
      <param name="input1" value="regr_inp.tabular"/>
      <param name="response_col" value="3"/>
      <param name="predictor_cols" value="1,2"/>
      <output name="out_file1" file="partialR_result.tabular"/>
    </test>
    
  </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 computes the Partial R squared for all possible variable subsets using the following formula:

**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. 



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,

- SSE (full: 1,2,..,i..,p-1) = Sum of Squares left out by the full set of predictors SSE(X1, X2 … Xp)
- 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).


The 4 columns in the output are described below:

- Column 1 (Model): denotes the variables present in the model
- Column 2 (R-sq): denotes the R-squared value corresponding to the model in Column 1
- 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.
- 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.

*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.*  
  
  </help>
</tool>