pca.py $input1 $var_cols $methodChoice.method $out_file1 $out_file2 #if $methodChoice.method == "svd": $methodChoice.scale #end if Eigenvectors of Correlation (princomp) Eigenvectors of Covariance (princomp) Singular Value Decomposition (prcomp) None Center only Scale only Center and Scale rpy

.. class:: infomark **TIP:** If your data is not TAB delimited, use *Edit Queries->Convert characters* ----- .. class:: infomark **What it does** This tool performs Principal Component Analysis on the given numeric input data using functions from R statistical package - 'princomp' function (for Eigenvector based solution) and 'prcomp' function (for Singular value decomposition based solution). It outputs two files, one containing the summary statistics of PCA, and the other containing biplots of the observations and principal components. *R Development Core Team (2009). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.* ----- .. class:: warningmark **Note** - This tool currently treats all 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: - Std. deviation: Standard deviations of the principal components - Loadings: a list of eigen-vectors/variable loadings - Scores: Scores of the input data on the principal components