comparison partialR_square.xml @ 1:2e7bc1bb2dbe draft default tip

Uploaded

0:ffcdde989859

  <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">

      <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,

- 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>

1:2e7bc1bb2dbe

  <expand macro="requirements" />
    <macros>
        <import>statistic_tools_macros.xml</import>
    </macros>
  <command interpreter="python">
<![CDATA[
    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">

      <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>
<![CDATA[

.. class:: infomark

**TIP:** If your data is not TAB delimited, use *Edit Datasets->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,

- 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>