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

Uploaded

0:ffcdde989859

    <expand macro="requirements" />
    <macros>
        <import>statistic_tools_macros.xml</import>
    </macros>
  <command interpreter="python">
    kcca.py 
      --input=$input1
      --output1=$out_file1
      --x_cols=$x_cols
      --y_cols=$y_cols
      --kernel=$kernelChoice.kernel

      --degree="None"
      --scale="None"
      --offset="None"
      --order="None"
      #end if
  </command>
  <inputs>
    <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/>
    <param name="x_cols" label="Select columns containing X variables " type="data_column" data_ref="input1" numerical="True" multiple="true" >
        <validator type="no_options" message="Please select at least one column."/>

        </when>
        <when value="anovadot">
            <param name="sigma" size="10" type="float" value="1" label="sigma" />
            <param name="degree" size="10" type="float" value="1" label="degree" />
        </when>
    </conditional>    
  </inputs>
  <outputs>
    <data format="input" name="out_file1" metadata_source="input1" />
  </outputs>
  <tests>

        <param name="sigma" value="0.5"/>
        <output name="out_file1" file="kcca_out2.tabular" compare="re_match"/>
    </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 uses functions from 'kernlab' library from R statistical package to perform Kernel Canonical Correlation Analysis (kCCA) on the input data. 

*Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software 11(9), 1-20. URL http://www.jstatsoft.org/v11/i09/*

-----

**Note**

This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.

  </help>
</tool>

1:2e7bc1bb2dbe

    <expand macro="requirements" />
    <macros>
        <import>statistic_tools_macros.xml</import>
    </macros>
  <command interpreter="python">
<![CDATA[
    kcca.py
      --input=$input1
      --output1=$out_file1
      --x_cols=$x_cols
      --y_cols=$y_cols
      --kernel=$kernelChoice.kernel

      --degree="None"
      --scale="None"
      --offset="None"
      --order="None"
      #end if
]]>
  </command>
  <inputs>
    <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/>
    <param name="x_cols" label="Select columns containing X variables " type="data_column" data_ref="input1" numerical="True" multiple="true" >
        <validator type="no_options" message="Please select at least one column."/>

        </when>
        <when value="anovadot">
            <param name="sigma" size="10" type="float" value="1" label="sigma" />
            <param name="degree" size="10" type="float" value="1" label="degree" />
        </when>
    </conditional>
  </inputs>
  <outputs>
    <data format="input" name="out_file1" metadata_source="input1" />
  </outputs>
  <tests>

        <param name="sigma" value="0.5"/>
        <output name="out_file1" file="kcca_out2.tabular" compare="re_match"/>
    </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 uses functions from 'kernlab' library from R statistical package to perform Kernel Canonical Correlation Analysis (kCCA) on the input data.

*Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software 11(9), 1-20. URL http://www.jstatsoft.org/v11/i09/*

-----

**Note**

This tool currently treats all variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results. Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.

]]>
  </help>
</tool>