iPE::Smoother::Kernel(3)

NAME
iPE::Smoother::Kernel - A kernel-Gaussian smoother

DESCRIPTION
This is a kernel smoother for the using a normal (Gaussian) density function. Two parameters are optionally passed into the instantiation of the smoother relating to the bandwidth of the smoothing. For each element of the smoothed distribution, the bandwitdth is found as the following function:

sigma(x) = max((a/n^5)*x, min_neighbor_distance(x))

These can be adjusted with the parameter a (errorConstant), and the parameter neighborRadius, inputted through the smoothing_data tag. The errorConstant modifies the first case of the bandwidth function (where n is the total number of counts), proportionally increasing the likelihood of the first one to be chosen. The neighborRadius adjusts how far from the current sample in the histogram distribution a neighbor is taken.

If nothing is passed, the default values of 0.5 and 8 are used, which were found sufficient for Augustus and iscan. For distributions with fewer possible values, it is recommended that you lower the neighborRadius.

For more information on this method see the Augustus thesis, _Gene Prediction with a Hidden Markov Model_, Mario Stanke 2003, or its reference, _Applied Smoothing Techniques for Data Analysis_, Bowman and Azzalini, 1997.

FUNCTIONS

init ()
    Initialize the constants used in this smoother.

smoothArray (aref)
    Smooths the data given according to the kernel-Gaussian smoother.

SEE ALSO
iPE::Smoother

AUTHOR
Bob Zimmermann (rpz@cse.wustl.edu)