diff cca.xml @ 0:ffcdde989859 draft

Uploaded

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/cca.xml	Tue Jul 29 06:30:45 2014 -0400
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+<tool id="cca1" name="Canonical Correlation Analysis" version="1.1.0">
+    <description> </description>
+    <expand macro="requirements" />
+    <macros>
+        <import>statistic_tools_macros.xml</import>
+    </macros>
+    <command interpreter="python">
+        cca.py 
+          $input1
+          $x_cols
+          $y_cols
+          $x_scale
+          $y_scale
+          $std_scores
+          $out_file1
+          $out_file2
+    </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."/>
+    </param>
+    <param name="y_cols" label="Select columns containing Y variables " type="data_column" data_ref="input1" numerical="True" multiple="true" >
+        <validator type="no_options" message="Please select at least one column."/>
+    </param>
+    <param name="x_scale" type="select" label="Type of Scaling for X variables" help="Can be used to center and/or scale variables">
+        <option value="none" selected="true">None</option>
+        <option value="center">Center only</option>
+        <option value="scale">Scale only</option>
+        <option value="both">Center and Scale</option>
+    </param>
+    <param name="y_scale" type="select" label="Type of Scaling for Y variables" help="Can be used to center and/or scale variables">
+        <option value="none" selected="true">None</option>
+        <option value="center">Center only</option>
+        <option value="scale">Scale only</option>
+        <option value="both">Center and Scale</option>
+    </param>
+    <param name="std_scores" type="select" label="Report standardized scores?" help="Selecting 'Yes' will rescale scores (and coefficients) to produce scores of unit variance">
+        <option value="no" selected="true">No</option>
+        <option value="yes">Yes</option>
+    </param>
+  </inputs>
+  <outputs>
+    <data format="input" name="out_file1" metadata_source="input1" />
+    <data format="pdf" name="out_file2" />
+  </outputs>
+  <tests>
+    <test>
+        <param name="input1" value="iris.tabular"/>
+        <param name="x_cols" value="3,4"/>
+        <param name="y_cols" value="1,2"/>
+        <param name="x_scale" value="both"/>
+        <param name="y_scale" value="scale"/>
+        <param name="std_scores" value="yes"/>
+        <output name="out_file1" file="cca_out1.tabular"/>
+        <output name="out_file2" file="cca_out2.pdf"/>
+    </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 'yacca' library from R statistical package to perform Canonical Correlation Analysis (CCA) on the input data. 
+It outputs two files, one containing the summary statistics of the performed CCA, and the other containing helioplots, which display structural loadings of X and Y variables on different canonical components.   
+
+*Carter T. Butts (2009). yacca: Yet Another Canonical Correlation Analysis Package. R package version 1.1.*
+
+-----
+
+.. class:: warningmark
+
+**Note**
+
+- This tool currently treats all predictor and response 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.
+
+- The summary statistics in the output are described below:
+
+  - correlation: Canonical correlation between the canonical variates (i.e. transformed variables)
+  - F-statistic: F-value obtained from F Test for Canonical Correlations Using Rao's Approximation
+  - p-value: denotes significance of canonical correlations
+  - Coefficients: represent the coefficients of X and Y variables on each canonical variate
+  - Loadings: represent the correlations between the original variables in each set and their respective canonical variates 
+  - CrossLoadings: represent the correlations between the original variables in each set and the opposite canonical variates 
+  
+  </help>
+</tool>