Package edu.cmu.tetrad.sem

Class DagScorer

java.lang.Object

edu.cmu.tetrad.sem.DagScorer

All Implemented Interfaces:

Scorer, TetradSerializable, Serializable

public final class DagScorer
extends Object
implements TetradSerializable, Scorer

Estimates a SemIm given a CovarianceMatrix and a SemPm. (A DataSet may be substituted for the CovarianceMatrix.) Uses regression to do the estimation, so this is only for DAG models. But the DAG model may be reset on the fly and the estimation redone. Variables whose parents have not changed will not be reestimated. Intended to speed up estimation for algorithm that require repeated estimation of DAG models over the same variables. Assumes all variables are measured.

Version:

$Id: $Id

Author:

josephramsey

See Also:

• Serialized Form

Constructor Summary

Constructors

Constructor	Description
DagScorer(DataSet dataSet)	Constructs a new SemEstimator that uses the specified optimizer.
DagScorer(ICovarianceMatrix covMatrix)	Constructs a new SemEstimator that uses the specified optimizer.

Method Summary

Modifier and Type	Method	Description
double	getBicScore()	getBicScore.
double	getChiSquare()	getChiSquare.
ICovarianceMatrix	getCovMatrix()	Getter for the field covMatrix.
DataSet	getDataSet()	Getter for the field dataSet.
int	getDof()	getDof.
Matrix	getEdgeCoef()	Getter for the field edgeCoef.
Matrix	getErrorCovar()	Getter for the field errorCovar.
SemIm	getEstSem()	getEstSem.
double	getFml()	The value of the maximum likelihood function for the getModel the model (Bollen 107).
List<Node>	getMeasuredNodes()	getMeasuredNodes.
int	getNumFreeParams()	getNumFreeParams.
double	getPValue()	getPValue.
Matrix	getSampleCovar()	Getter for the field sampleCovar.
int	getSampleSize()	getSampleSize.
List<Node>	getVariables()	Getter for the field variables.
double	score(Graph dag)	Scores the given DAG using the implemented algorithm.
static Scorer	serializableInstance()	Generates a simple exemplar of this class to test serialization.
String	toString()	toString.

Methods inherited from class java.lang.Object

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

Constructor Details

DagScorer

public DagScorer(DataSet dataSet)

Constructs a new SemEstimator that uses the specified optimizer.

Parameters:

dataSet - a DataSet, all of whose variables are contained in the given SemPm. (They are identified by name.)

DagScorer

public DagScorer(ICovarianceMatrix covMatrix)

Constructs a new SemEstimator that uses the specified optimizer.

Parameters:

covMatrix - a covariance matrix, all of whose variables are contained in the given SemPm. (They are identified by name.)

Method Details

serializableInstance

public static Scorer serializableInstance()

Generates a simple exemplar of this class to test serialization.

Returns:

a Scorer object

score

public double score(Graph dag)

Scores the given DAG using the implemented algorithm.

Specified by:

score in interface Scorer

Parameters:

dag - the DAG to be scored

Returns:

the score of the DAG

getCovMatrix

public ICovarianceMatrix getCovMatrix()

Getter for the field covMatrix.

Specified by:

getCovMatrix in interface Scorer

Returns:

a ICovarianceMatrix object

toString

public String toString()

toString.

Specified by:

toString in interface Scorer

Overrides:

toString in class Object

Returns:

a string representation of the Sem.

getFml

public double getFml()

The value of the maximum likelihood function for the getModel the model (Bollen 107). To optimize, this should be minimized.

Specified by:

getFml in interface Scorer

Returns:

a double

getBicScore

public double getBicScore()

getBicScore.

Specified by:

getBicScore in interface Scorer

Returns:

BIC score, calculated as chisq - dof. This is equal to getFullBicScore() up to a constant.

getChiSquare

public double getChiSquare()

getChiSquare.

Specified by:

getChiSquare in interface Scorer

Returns:

the chi square value for the model.

getPValue

public double getPValue()

getPValue.

Specified by:

getPValue in interface Scorer

Returns:

the p-value for the model.

getDataSet

public DataSet getDataSet()

Getter for the field dataSet.

Specified by:

getDataSet in interface Scorer

Returns:

a DataSet object

getNumFreeParams

public int getNumFreeParams()

getNumFreeParams.

Specified by:

getNumFreeParams in interface Scorer

Returns:

a int

getDof

public int getDof()

getDof.

Specified by:

getDof in interface Scorer

Returns:

a int

getSampleSize

public int getSampleSize()

getSampleSize.

Specified by:

getSampleSize in interface Scorer

Returns:

a int

getMeasuredNodes

public List<Node> getMeasuredNodes()

getMeasuredNodes.

Specified by:

getMeasuredNodes in interface Scorer

Returns:

a List object

getSampleCovar

public Matrix getSampleCovar()

Getter for the field sampleCovar.

Specified by:

getSampleCovar in interface Scorer

Returns:

a Matrix object

getEdgeCoef

public Matrix getEdgeCoef()

Getter for the field edgeCoef.

Specified by:

getEdgeCoef in interface Scorer

Returns:

a Matrix object

getErrorCovar

public Matrix getErrorCovar()

Getter for the field errorCovar.

Specified by:

getErrorCovar in interface Scorer

Returns:

a Matrix object

getVariables

public List<Node> getVariables()

Getter for the field variables.

Specified by:

getVariables in interface Scorer

Returns:

a List object

getEstSem

public SemIm getEstSem()

getEstSem.

Specified by:

getEstSem in interface Scorer

Returns:

a SemIm object