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author devteam
date Mon, 19 May 2014 12:34:48 -0400
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1 <tool id="cca1" name="Canonical Correlation Analysis" version="1.0.0">
2 <description> </description>
3 <requirements>
4 <requirement type="package" version="2.11.0">R</requirement>
5 <requirement type="package" version="1.7.1">numpy</requirement>
6 <requirement type="package" version="1.0">yacca</requirement>
7 <requirement type="package" version="1.0.3">rpy</requirement>
8 </requirements>
9 <command interpreter="python">
10 cca.py
11 $input1
12 $x_cols
13 $y_cols
14 $x_scale
15 $y_scale
16 $std_scores
17 $out_file1
18 $out_file2
19 </command>
20 <inputs>
21 <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/>
22 <param name="x_cols" label="Select columns containing X variables " type="data_column" data_ref="input1" numerical="True" multiple="true" >
23 <validator type="no_options" message="Please select at least one column."/>
24 </param>
25 <param name="y_cols" label="Select columns containing Y variables " type="data_column" data_ref="input1" numerical="True" multiple="true" >
26 <validator type="no_options" message="Please select at least one column."/>
27 </param>
28 <param name="x_scale" type="select" label="Type of Scaling for X variables" help="Can be used to center and/or scale variables">
29 <option value="none" selected="true">None</option>
30 <option value="center">Center only</option>
31 <option value="scale">Scale only</option>
32 <option value="both">Center and Scale</option>
33 </param>
34 <param name="y_scale" type="select" label="Type of Scaling for Y variables" help="Can be used to center and/or scale variables">
35 <option value="none" selected="true">None</option>
36 <option value="center">Center only</option>
37 <option value="scale">Scale only</option>
38 <option value="both">Center and Scale</option>
39 </param>
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">
41 <option value="no" selected="true">No</option>
42 <option value="yes">Yes</option>
43 </param>
44 </inputs>
45 <outputs>
46 <data format="input" name="out_file1" metadata_source="input1" />
47 <data format="pdf" name="out_file2" />
48 </outputs>
49 <tests>
50 <test>
51 <param name="input1" value="iris.tabular"/>
52 <param name="x_cols" value="3,4"/>
53 <param name="y_cols" value="1,2"/>
54 <param name="x_scale" value="both"/>
55 <param name="y_scale" value="scale"/>
56 <param name="std_scores" value="yes"/>
57 <output name="out_file1" file="cca_out1.tabular"/>
58 <output name="out_file2" file="cca_out2.pdf"/>
59 </test>
60 </tests>
61 <help>
62
63
64 .. class:: infomark
65
66 **TIP:** If your data is not TAB delimited, use *Edit Datasets-&gt;Convert characters*
67
68 -----
69
70 .. class:: infomark
71
72 **What it does**
73
74 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.
75
76 *Carter T. Butts (2009). yacca: Yet Another Canonical Correlation Analysis Package. R package version 1.1.*
77
78 -----
79
80 .. class:: warningmark
81
82 **Note**
83
84 - This tool currently treats all predictor and response variables as continuous numeric variables. Running the tool on categorical variables might result in incorrect results.
85
86 - Rows containing non-numeric (or missing) data in any of the chosen columns will be skipped from the analysis.
87
88 - The summary statistics in the output are described below:
89
90 - correlation: Canonical correlation between the canonical variates (i.e. transformed variables)
91 - F-statistic: F-value obtained from F Test for Canonical Correlations Using Rao's Approximation
92 - p-value: denotes significance of canonical correlations
93 - Coefficients: represent the coefficients of X and Y variables on each canonical variate
94 - Loadings: represent the correlations between the original variables in each set and their respective canonical variates
95 - CrossLoadings: represent the correlations between the original variables in each set and the opposite canonical variates
96
97 </help>
98 </tool>