Package edu.cmu.tetrad.sem

Class StandardizedSemIm

java.lang.Object

edu.cmu.tetrad.sem.StandardizedSemIm

All Implemented Interfaces:

Simulator, TetradSerializable, Serializable

public class StandardizedSemIm
extends Object
implements Simulator

A special SEM model in which variances of variables are always 1 and means of variables are always 0. In order to ensure that means of variables are always zero, means or error terms are set to zero. (They are alway Gaussian for this model.) Connection functions are always linear. In order to ensure that variances of variables are always 1, only coefficients are allowed to change, and the error terms take up the slack. Becuase of this constraint, given settings of other freeParameters, the range of a given parameter is always bounded above and below. The user may query this range and set set the value of the coefficient to anything within this range. The SEM is initialized from a linear, gaussian SEM by calculating (or estimating) what the coefficients would be if a data set were simulated from that SEM, standardized, and reestimated with the same SEM PM. The coefficients of such an estimated SEM PM are used to initialize the standardized SEM, repeating if necessary (due to possible noise issues) to get coefficients for which all errors variances can be calculated. (Variances need to be >= 0 for Normal distributions.) This produces a set of coefficients that are viable candidates for the standardized SEM. From there, the user cannot make any change that does not also allow for a standardized SEM to be defined, with error variances taking up the slack. Thus, the standardized SEM can never go "out of bounds."

Currently we are not allowing bidirected edges in the SEM graph.

Version:

$Id: $Id

Author:

josephramsey

See Also:

• Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static enum	StandardizedSemIm.Initialization	The initialization method for the model.
static final class	StandardizedSemIm.ParameterRange	Stores a coefficient range--i.e.

Constructor Summary

Constructors

Constructor	Description
StandardizedSemIm(SemIm im, StandardizedSemIm.Initialization initialization, Parameters parameters)	Constructs a new standardized SEM IM from the freeParameters in the given SEM IM.
StandardizedSemIm(SemIm im, Parameters parameters)	Constructs a new standardized SEM IM, initializing from the freeParameters in the given SEM IM.

Method Summary

Modifier and Type	Method	Description
boolean	containsParameter(Edge edge)	containsParameter.
StandardizedSemIm.ParameterRange	getCoefficientRange(Node a, Node b)	getCoefficientRange.
StandardizedSemIm.ParameterRange	getCovarianceRange(Node a, Node b)	getCovarianceRange.
double	getEdgeCoef(Node a, Node b)	Getter for the field edgeCoef.
double	getErrorCovariance(Node a, Node b)	getErrorCovariance.
List<Node>	getErrorNodes()	getErrorNodes.
double	getErrorVariance(Node error)	getErrorVariance.
Matrix	getImplCovar()	Getter for the field implCovar.
Matrix	getImplCovarMeas()	Getter for the field implCovarMeas.
List<Node>	getMeasuredNodes()	The list of measured nodes for the semPm.
StandardizedSemIm.ParameterRange	getParameterRange(Edge edge)	getParameterRange.
double	getParameterValue(Edge edge)	getParameterValue.
int	getSampleSize()	Getter for the field sampleSize.
SemPm	getSemPm()	Getter for the field semPm.
List<Node>	getVariableNodes()	getVariableNodes.
double[]	means()	means.
static StandardizedSemIm	serializableInstance()	Generates a simple exemplar of this class to test serialization.
boolean	setEdgeCoefficient(Node a, Node b, double coef)	Sets the coefficient for the a->b edge to the given coefficient, if within range.
boolean	setErrorCovariance(Node a, Node b, double covar)	Sets the covariance for the a<->b edge to the given covariance, if within range.
void	setParameterValue(Edge edge, double value)	setParameterValue.
DataSet	simulateData(int sampleSize, boolean latentDataSaved)	Simulates data from the model associated with this object.
DataSet	simulateDataReducedForm(int sampleSize, boolean latentDataSaved)	simulateDataReducedForm.
String	toString()	toString.

Methods inherited from class java.lang.Object

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

Constructor Details

StandardizedSemIm

public StandardizedSemIm(SemIm im,
 Parameters parameters)

Constructs a new standardized SEM IM, initializing from the freeParameters in the given SEM IM.

Parameters:

im - The SEM IM that the freeParameters will be initialized from.

parameters - a Parameters object

StandardizedSemIm

public StandardizedSemIm(SemIm im,
 StandardizedSemIm.Initialization initialization,
 Parameters parameters)

Constructs a new standardized SEM IM from the freeParameters in the given SEM IM.

Parameters:

im - Stop asking me for these things! The given SEM IM!!!

initialization - CALCULATE_FROM_SEM if the initial values will be calculated from the given SEM IM;

parameters - a Parameters object

Method Details

serializableInstance

public static StandardizedSemIm serializableInstance()

Generates a simple exemplar of this class to test serialization.

Returns:

a StandardizedSemIm object

getSampleSize

public int getSampleSize()

Getter for the field sampleSize.

Returns:

a int

containsParameter

public boolean containsParameter(Edge edge)

containsParameter.

Parameters:

edge - a Edge object

Returns:

a boolean

setEdgeCoefficient

public boolean setEdgeCoefficient(Node a,
 Node b,
 double coef)

Sets the coefficient for the a->b edge to the given coefficient, if within range. Otherwise does nothing.

Parameters:

a - a -> b

b - a -> b

coef - The coefficient of a -> b.

Returns:

true if the coefficent was set (i.e. was within range), false if not.

setErrorCovariance

public boolean setErrorCovariance(Node a,
 Node b,
 double covar)

Sets the covariance for the a<->b edge to the given covariance, if within range. Otherwise does nothing.

Parameters:

a - a <-> b

b - a <-> b

covar - The covariance of a <-> b.

Returns:

true if the coefficent was set (i.e. was within range), false if not.

getEdgeCoef

public double getEdgeCoef(Node a,
 Node b)

Getter for the field edgeCoef.

Parameters:

a - a->b

b - a->b

Returns:

The coefficient for a->b.

getErrorCovariance

public double getErrorCovariance(Node a,
 Node b)

getErrorCovariance.

Parameters:

a - a->b

b - a->b

Returns:

The coefficient for a->b.

getParameterValue

public double getParameterValue(Edge edge)

getParameterValue.

Parameters:

edge - a Edge object

Returns:

a double

setParameterValue

public void setParameterValue(Edge edge,
 double value)

setParameterValue.

Parameters:

edge - a Edge object

value - a double

getCoefficientRange

public StandardizedSemIm.ParameterRange getCoefficientRange(Node a,
 Node b)

getCoefficientRange.

Parameters:

a - a Node object

b - a Node object

Returns:

a StandardizedSemIm.ParameterRange object

getCovarianceRange

public StandardizedSemIm.ParameterRange getCovarianceRange(Node a,
 Node b)

getCovarianceRange.

Parameters:

a - a Node object

b - a Node object

Returns:

a StandardizedSemIm.ParameterRange object

getParameterRange

public StandardizedSemIm.ParameterRange getParameterRange(Edge edge)

getParameterRange.

Parameters:

edge - a->b or a<->b.

Returns:

the range of the covariance parameter for a->b or a<->b.

getErrorVariance

public double getErrorVariance(Node error)

getErrorVariance.

Parameters:

error - The error term. A node with NodeType.ERROR.

Returns:

the error variance for the given error term. THIS IS NOT A PARAMETER OF THE MODEL! Its value is simply calculated from the given coefficients of the model. Returns Double.NaN if the error variance cannot be computed.

toString

public String toString()

toString.

Overrides:

toString in class Object

Returns:

a string representation of the coefficients and variances of the model.

getVariableNodes

public List<Node> getVariableNodes()

getVariableNodes.

Returns:

the list of variable nodes of the model, in order.

means

public double[] means()

means.

Returns:

For compatibility only. Returns the variable means of the model. These are always zero, since this is a standardized model. THESE ARE ALSO NOT PARAMETERS OF THE MODEL. ONLY THE COEFFICIENTS ARE PARAMETERS.

simulateData

public DataSet simulateData(int sampleSize,
 boolean latentDataSaved)

Simulates data from the model associated with this object.

A convenience method, in case we want to change out mind about how to simulate. For instance, it's unclear yet whether we can allow nongaussian errors, so we don't know yet whether the reduced form method is needed.

Specified by:

simulateData in interface Simulator

Parameters:

sampleSize - the number of rows to simulate.

latentDataSaved - if true, latent variables are saved in the data set.

Returns:

the simulated data set.

simulateDataReducedForm

public DataSet simulateDataReducedForm(int sampleSize,
 boolean latentDataSaved)

simulateDataReducedForm.

Parameters:

sampleSize - a int

latentDataSaved - a boolean

Returns:

a DataSet object

getImplCovar

public Matrix getImplCovar()

Getter for the field implCovar.

Returns:

a copy of the implied covariance matrix over all the variables.

getImplCovarMeas

public Matrix getImplCovarMeas()

Getter for the field implCovarMeas.

Returns:

a copy of the implied covariance matrix over the measured variables only.

getMeasuredNodes

public List<Node> getMeasuredNodes()

The list of measured nodes for the semPm. (Unmodifiable.)

Returns:

a List object

getErrorNodes

public List<Node> getErrorNodes()

getErrorNodes.

Returns:

a List object

getSemPm

public SemPm getSemPm()

Getter for the field semPm.

Returns:

a copy of the SEM PM.