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

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author iuc
date Fri, 09 Jan 2015 12:56:07 -0500
parents ffcdde989859
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0:ffcdde989859 1:2e7bc1bb2dbe
3 <expand macro="requirements" /> 3 <expand macro="requirements" />
4 <macros> 4 <macros>
5 <import>statistic_tools_macros.xml</import> 5 <import>statistic_tools_macros.xml</import>
6 </macros> 6 </macros>
7 <command interpreter="python"> 7 <command interpreter="python">
8 cca.py 8 <![CDATA[
9 cca.py
9 $input1 10 $input1
10 $x_cols 11 $x_cols
11 $y_cols 12 $y_cols
12 $x_scale 13 $x_scale
13 $y_scale 14 $y_scale
14 $std_scores 15 $std_scores
15 $out_file1 16 $out_file1
16 $out_file2 17 $out_file2
18 ]]>
17 </command> 19 </command>
18 <inputs> 20 <inputs>
19 <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/> 21 <param format="tabular" name="input1" type="data" label="Select data" help="Dataset missing? See TIP below."/>
20 <param name="x_cols" label="Select columns containing X variables " type="data_column" data_ref="input1" numerical="True" multiple="true" > 22 <param name="x_cols" label="Select columns containing X variables " type="data_column" data_ref="input1" numerical="True" multiple="true" >
21 <validator type="no_options" message="Please select at least one column."/> 23 <validator type="no_options" message="Please select at least one column."/>
55 <output name="out_file1" file="cca_out1.tabular"/> 57 <output name="out_file1" file="cca_out1.tabular"/>
56 <output name="out_file2" file="cca_out2.pdf"/> 58 <output name="out_file2" file="cca_out2.pdf"/>
57 </test> 59 </test>
58 </tests> 60 </tests>
59 <help> 61 <help>
62 <![CDATA[
60 63
61 64
62 .. class:: infomark 65 .. class:: infomark
63 66
64 **TIP:** If your data is not TAB delimited, use *Edit Datasets-&gt;Convert characters* 67 **TIP:** If your data is not TAB delimited, use *Edit Datasets->Convert characters*
65 68
66 ----- 69 -----
67 70
68 .. class:: infomark 71 .. class:: infomark
69 72
70 **What it does** 73 **What it does**
71 74
72 This tool uses functions from 'yacca' library from R statistical package to perform Canonical Correlation Analysis (CCA) on the input data. 75 This tool uses functions from 'yacca' library from R statistical package to perform Canonical Correlation Analysis (CCA) on the input data.
73 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. 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.
74 77
75 *Carter T. Butts (2009). yacca: Yet Another Canonical Correlation Analysis Package. R package version 1.1.* 78 *Carter T. Butts (2009). yacca: Yet Another Canonical Correlation Analysis Package. R package version 1.1.*
76 79
77 ----- 80 -----
78 81
88 91
89 - correlation: Canonical correlation between the canonical variates (i.e. transformed variables) 92 - correlation: Canonical correlation between the canonical variates (i.e. transformed variables)
90 - F-statistic: F-value obtained from F Test for Canonical Correlations Using Rao's Approximation 93 - F-statistic: F-value obtained from F Test for Canonical Correlations Using Rao's Approximation
91 - p-value: denotes significance of canonical correlations 94 - p-value: denotes significance of canonical correlations
92 - Coefficients: represent the coefficients of X and Y variables on each canonical variate 95 - Coefficients: represent the coefficients of X and Y variables on each canonical variate
93 - Loadings: represent the correlations between the original variables in each set and their respective canonical variates 96 - Loadings: represent the correlations between the original variables in each set and their respective canonical variates
94 - CrossLoadings: represent the correlations between the original variables in each set and the opposite canonical variates 97 - CrossLoadings: represent the correlations between the original variables in each set and the opposite canonical variates
95 98
99 ]]>
96 </help> 100 </help>
97 </tool> 101 </tool>