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1 <tool id="cca1" name="Canonical Correlation Analysis" version="1.1.0">
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2 <description> </description>
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3 <expand macro="requirements" />
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4 <macros>
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5 <import>statistic_tools_macros.xml</import>
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6 </macros>
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7 <command interpreter="python">
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8 <![CDATA[
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9 cca.py
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10 $input1
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11 $x_cols
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12 $y_cols
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13 $x_scale
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14 $y_scale
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15 $std_scores
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16 $out_file1
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17 $out_file2
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18 ]]>
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19 </command>
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20 <inputs>
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21 <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/>
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22 <param name="x_cols" label="Select columns containing X variables " type="data_column" data_ref="input1" numerical="True" multiple="true" >
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23 <validator type="no_options" message="Please select at least one column."/>
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24 </param>
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25 <param name="y_cols" label="Select columns containing Y variables " type="data_column" data_ref="input1" numerical="True" multiple="true" >
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26 <validator type="no_options" message="Please select at least one column."/>
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27 </param>
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28 <param name="x_scale" type="select" label="Type of Scaling for X variables" help="Can be used to center and/or scale variables">
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29 <option value="none" selected="true">None</option>
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30 <option value="center">Center only</option>
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31 <option value="scale">Scale only</option>
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32 <option value="both">Center and Scale</option>
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33 </param>
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34 <param name="y_scale" type="select" label="Type of Scaling for Y variables" help="Can be used to center and/or scale variables">
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35 <option value="none" selected="true">None</option>
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36 <option value="center">Center only</option>
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37 <option value="scale">Scale only</option>
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38 <option value="both">Center and Scale</option>
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39 </param>
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40 <param name="std_scores" type="select" label="Report standardized scores?" help="Selecting 'Yes' will rescale scores (and coefficients) to produce scores of unit variance">
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41 <option value="no" selected="true">No</option>
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42 <option value="yes">Yes</option>
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43 </param>
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44 </inputs>
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45 <outputs>
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46 <data format="input" name="out_file1" metadata_source="input1" />
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47 <data format="pdf" name="out_file2" />
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48 </outputs>
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49 <tests>
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50 <test>
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51 <param name="input1" value="iris.tabular"/>
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52 <param name="x_cols" value="3,4"/>
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53 <param name="y_cols" value="1,2"/>
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54 <param name="x_scale" value="both"/>
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55 <param name="y_scale" value="scale"/>
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56 <param name="std_scores" value="yes"/>
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57 <output name="out_file1" file="cca_out1.tabular"/>
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58 <output name="out_file2" file="cca_out2.pdf"/>
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59 </test>
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60 </tests>
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61 <help>
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62 <![CDATA[
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63
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64
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65 .. class:: infomark
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66
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67 **TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters*
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68
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69 -----
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70
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71 .. class:: infomark
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72
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73 **What it does**
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74
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75 This tool uses functions from 'yacca' library from R statistical package to perform Canonical Correlation Analysis (CCA) on the input data.
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76 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.
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77
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78 *Carter T. Butts (2009). yacca: Yet Another Canonical Correlation Analysis Package. R package version 1.1.*
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79
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80 -----
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81
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82 .. class:: warningmark
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83
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84 **Note**
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85
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86 - This tool currently treats all predictor and response variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results.
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87
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88 - Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
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89
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90 - The summary statistics in the output are described below:
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91
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92 - correlation: Canonical correlation between the canonical variates (i.e. transformed variables)
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93 - F-statistic: F-value obtained from F Test for Canonical Correlations Using Rao's Approximation
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94 - p-value: denotes significance of canonical correlations
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95 - Coefficients: represent the coefficients of X and Y variables on each canonical variate
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96 - Loadings: represent the correlations between the original variables in each set and their respective canonical variates
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97 - CrossLoadings: represent the correlations between the original variables in each set and the opposite canonical variates
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98
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99 ]]>
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100 </help>
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101 </tool>
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