kpca.py
--input=$input1
--output1=$out_file1
--output2=$out_file2
--var_cols=$var_cols
--kernel=$kernelChoice.kernel
--features=$features
#if $kernelChoice.kernel == "rbfdot" or $kernelChoice.kernel == "anovadot":
--sigma=$kernelChoice.sigma
--degree="None"
--scale="None"
--offset="None"
--order="None"
#elif $kernelChoice.kernel == "polydot":
--sigma="None"
--degree=$kernelChoice.degree
--scale=$kernelChoice.scale
--offset=$kernelChoice.offset
--order="None"
#elif $kernelChoice.kernel == "tanhdot":
--sigma="None"
--degree="None"
--scale=$kernelChoice.scale
--offset=$kernelChoice.offset
--order="None"
#elif $kernelChoice.kernel == "besseldot":
--sigma=$kernelChoice.sigma
--degree=$kernelChoice.degree
--scale="None"
--offset="None"
--order=$kernelChoice.order
#elif $kernelChoice.kernel == "anovadot":
--sigma=$kernelChoice.sigma
--degree=$kernelChoice.degree
--scale="None"
--offset="None"
--order="None"
#else:
--sigma="None"
--degree="None"
--scale="None"
--offset="None"
--order="None"
#end if
rpy
.. class:: infomark
**TIP:** If your data is not TAB delimited, use *Edit Queries->Convert characters*
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.. class:: infomark
**What it does**
This tool uses functions from 'kernlab' library from R statistical package to perform Kernel Principal Component Analysis (kPCA) on the input data. It outputs two files, one containing the summary statistics of the performed kPCA, and the other containing a scatterplot matrix of rotated values reported by kPCA.
*Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software 11(9), 1-20. URL http://www.jstatsoft.org/v11/i09/*
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.. 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.