magpie.data.utilities.filters

Class PredictionOutlierFilter

java.lang.Object

magpie.data.utilities.filters.BaseDatasetFilter

magpie.data.utilities.filters.PredictionOutlierFilter

All Implemented Interfaces:

java.io.Serializable, Options

public class PredictionOutlierFilter
extends BaseDatasetFilter

Filters entries based on whether they are outliers. Uses RobustRegressionUtility and methods described by Motulsky and Brown to detect outliers. Data must have a both a measured and predicted class variable.

Usage: [-q <Q>] [-k <K> [-frac <fraction>]
Q: Target false discovery rate
K: Number of fitting parameters in model
fraction: Fraction of entries to check for being an outlier (starts with those with largest errors)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
PredictionOutlierFilter()

Method Summary

Modifier and Type	Method and Description
boolean[]	label(Dataset D) Given a dataset, determine which entries passes the filter.
java.lang.String	printUsage() Print out required format for options.
void	setK(int K) Define number of fitting parameters in model.
void	setOptions(java.util.List<java.lang.Object> OptionsObj) Set any options for this object.
void	setQ(double Q) Set target False Discovery rate.
void	train(Dataset TrainingSet) Train a dataset splitter, if necessary

Methods inherited from class magpie.data.utilities.filters.BaseDatasetFilter

filter, parallelLabel, parallelMinimum, setExclude, toExclude

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

PredictionOutlierFilter

public PredictionOutlierFilter()

Method Detail

setOptions

public void setOptions(java.util.List<java.lang.Object> OptionsObj)
                throws java.lang.Exception

Description copied from interface: Options

Set any options for this object. If parsing the options fails, return an Exception with the usage as its message.

Parameters:

OptionsObj - Array of options as Objects - can be null

Throws:

java.lang.Exception - if problem with inputs

printUsage

public java.lang.String printUsage()

Description copied from interface: Options

Print out required format for options. For consistency, should start with "Usage: ".

Returns:

Usage for this function

setQ

public void setQ(double Q)

Set target False Discovery rate. See RobustRegressionUtility.

Parameters:

Q - Target FDR

setK

public void setK(int K)

Define number of fitting parameters in model. See RobustRegressionUtility.

Parameters:

K - Number of fitting parameters

label

public boolean[] label(Dataset D)

Description copied from class: BaseDatasetFilter

Given a dataset, determine which entries passes the filter. Please ensure that D remains unaltered (clone if you must)

Specified by:

label in class BaseDatasetFilter

Parameters:

D - Dataset to be labeled

Returns:

A list of booleans marking whether an entry passes

train

public void train(Dataset TrainingSet)

Description copied from class: BaseDatasetFilter

Train a dataset splitter, if necessary

Specified by:

train in class BaseDatasetFilter

Parameters:

TrainingSet - Dataset to use for training