Package edu.cmu.tetrad.search.utils

Class TsUtils

java.lang.Object

edu.cmu.tetrad.search.utils.TsUtils

public class TsUtils
extends Object

Contains some utilities for doing autoregression. Should probably be improved by somebody.

Version:

$Id: $Id

Author:

josephramsey, danielmalinsky (some improvements)

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	TsUtils.VarResult	Gives a result consisting of the residuals and collapsed var graphs.

Method Summary

Modifier and Type	Method	Description
static DataSet	addIndex(DataSet data)	Creates new time series dataset from the given one with index variable (e.g., time)
static boolean	allEigenvaluesAreSmallerThanOneInModulus(Matrix mat)	allEigenvaluesAreSmallerThanOneInModulus.
static DataSet	ar(DataSet timeSeries, int numLags)	ar.
static DataSet	ar2(DataSet timeSeries, int numLags)	ar2.
static DataSet	createLagData(DataSet data, int numLags)	Creates new time series dataset from the given one (fixed to deal with mixed datasets)
static DataSet	createShiftedData(DataSet data, int[] shifts)	createShiftedData.
static DataSet	difference(DataSet data, int d)	Calculates the dth difference of the given data.
static int	getIndex(String s)	getIndex.
static Knowledge	getKnowledge(Graph graph)	getKnowledge.
static int	getLag(String s)	getLag.
static String	getNameNoLag(Object obj)	getNameNoLag.
static String	getPrefix(String s)	getPrefix.
static double[]	getSelfLoopCoefs(DataSet timeSeries)	getSelfLoopCoefs.
static TimeLagGraph	graphToLagGraph(Graph _graph, int numLags)	graphToLagGraph.
static TsUtils.VarResult	structuralVar(DataSet timeSeries, int numLags)	structuralVar.
static double	sumOfArCoefficients(DataSet timeSeries, int numLags)	sumOfArCoefficients.

Methods inherited from class java.lang.Object

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

Method Details

ar

public static DataSet ar(DataSet timeSeries,
 int numLags)

ar.

Parameters:

timeSeries - a DataSet object

numLags - a int

Returns:

the VAR residuals of the given time series with the given number of lags. That is, every variable at the model lag is regressed onto every variable at previous lags, up to the given number of lags, and the residuals of these regressions for each variable are returned.

ar2

public static DataSet ar2(DataSet timeSeries,
 int numLags)

ar2.

Parameters:

timeSeries - a DataSet object

numLags - a int

Returns:

a DataSet object

structuralVar

public static TsUtils.VarResult structuralVar(DataSet timeSeries,
 int numLags)
                                       throws InterruptedException

structuralVar.

Parameters:

timeSeries - a DataSet object

numLags - a int

Returns:

a TsUtils.VarResult object

Throws:

InterruptedException

createShiftedData

public static DataSet createShiftedData(DataSet data,
 int[] shifts)

createShiftedData.

Parameters:

data - a DataSet object

shifts - an array of objects

Returns:

a DataSet object

getSelfLoopCoefs

public static double[] getSelfLoopCoefs(DataSet timeSeries)

getSelfLoopCoefs.

Parameters:

timeSeries - a DataSet object

Returns:

an array of objects

sumOfArCoefficients

public static double sumOfArCoefficients(DataSet timeSeries,
 int numLags)

sumOfArCoefficients.

Parameters:

timeSeries - a DataSet object

numLags - a int

Returns:

a double

difference

public static DataSet difference(DataSet data,
 int d)

Calculates the dth difference of the given data. If d = 0, the original data is returned. If d = 1, the data (with one fewer rows) is returned, with each row subtracted from its successor. If d = 1, the same operation is applied to the result of d = 1. And so on.

Parameters:

data - the data to be differenced.

d - the number of differences to take, >= 0.

Returns:

the differenced data.

createLagData

public static DataSet createLagData(DataSet data,
 int numLags)

Creates new time series dataset from the given one (fixed to deal with mixed datasets)

Parameters:

data - a DataSet object

numLags - a int

Returns:

a DataSet object

addIndex

public static DataSet addIndex(DataSet data)

Creates new time series dataset from the given one with index variable (e.g., time)

Parameters:

data - a DataSet object

Returns:

a DataSet object

graphToLagGraph

public static TimeLagGraph graphToLagGraph(Graph _graph,
 int numLags)

graphToLagGraph.

Parameters:

_graph - a Graph object

numLags - a int

Returns:

a TimeLagGraph object

getNameNoLag

public static String getNameNoLag(Object obj)

getNameNoLag.

Parameters:

obj - a Object object

Returns:

a String object

getPrefix

public static String getPrefix(String s)

getPrefix.

Parameters:

s - a String object

Returns:

a String object

getIndex

public static int getIndex(String s)

getIndex.

Parameters:

s - a String object

Returns:

a int

getLag

public static int getLag(String s)

getLag.

Parameters:

s - a String object

Returns:

a int

getKnowledge

public static Knowledge getKnowledge(Graph graph)

getKnowledge.

Parameters:

graph - a Graph object

Returns:

a Knowledge object

allEigenvaluesAreSmallerThanOneInModulus

public static boolean allEigenvaluesAreSmallerThanOneInModulus(Matrix mat)

allEigenvaluesAreSmallerThanOneInModulus.

Parameters:

mat - a Matrix object

Returns:

a boolean