Mercurial > repos > iuc > rpy_statistics_collection
diff cca.xml @ 1:2e7bc1bb2dbe draft default tip
Uploaded
| author | iuc |
|---|---|
| date | Fri, 09 Jan 2015 12:56:07 -0500 |
| parents | ffcdde989859 |
| children |
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--- a/cca.xml Tue Jul 29 06:30:45 2014 -0400 +++ b/cca.xml Fri Jan 09 12:56:07 2015 -0500 @@ -1,97 +1,101 @@ -<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->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> +<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"> +<![CDATA[ + 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> +<![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 '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>