com.itemanalysis.psychometrics.distribution

Class AbstractDistributionApproximation

java.lang.Object

com.itemanalysis.psychometrics.distribution.AbstractDistributionApproximation

All Implemented Interfaces:

DistributionApproximation

Direct Known Subclasses:

ContinuousDistributionApproximation, NormalDistributionApproximation, UniformDistributionApproximation, UserSuppliedDistributionApproximation

public abstract class AbstractDistributionApproximation
extends java.lang.Object
implements DistributionApproximation

Field Summary

Fields

Modifier and Type	Field and Description
protected double	max
protected double	min
protected org.apache.commons.math3.distribution.NormalDistribution	normal
protected int	numberOfPoints
protected double[]	points
protected double	range
protected double	step
protected double[]	weights

Constructor Summary

Constructors

Constructor and Description
AbstractDistributionApproximation()

Method Summary

Modifier and Type	Method and Description
double[]	evaluate() Gets and array of density values.
double	getDensityAt(int index) Gets a density value at the specified index of the array.
double	getMaximum()
double	getMean()
double	getMinimum()
int	getNumberOfPoints() Gets the number of evaluation points (and corresponding number of density values).
double	getPointAt(int index) Gets an evaluation points at the specified index of the array.
double[]	getPoints() Gets an array of evaluation points.
double	getStandardDeviation()
double	getVariance()
void	setNormalPointsAndWeights(double mean, double sd)
void	setUniformPointsAndWeights()
double[]	standardize(boolean keepPoints) Uses current quadrature points and weights to compute the mean and standard deviation of the density, and then standardizes the distribution to have a mean of zero and a standard deviation of one.

Methods inherited from class java.lang.Object

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

Methods inherited from interface com.itemanalysis.psychometrics.distribution.DistributionApproximation

setDensityAt, setPointAt

Field Detail

numberOfPoints

protected int numberOfPoints

min

protected double min

max

protected double max

range

protected double range

step

protected double step

points

protected double[] points

weights

protected double[] weights

normal

protected org.apache.commons.math3.distribution.NormalDistribution normal

Constructor Detail

AbstractDistributionApproximation

public AbstractDistributionApproximation()

Method Detail

setNormalPointsAndWeights

public void setNormalPointsAndWeights(double mean,
                                      double sd)

setUniformPointsAndWeights

public void setUniformPointsAndWeights()

getPoints

public double[] getPoints()

Description copied from interface: DistributionApproximation

Gets an array of evaluation points.

Specified by:

getPoints in interface DistributionApproximation

Returns:

evaluation points.

evaluate

public double[] evaluate()

Description copied from interface: DistributionApproximation

Gets and array of density values.

Specified by:

evaluate in interface DistributionApproximation

Returns:

density values.

getPointAt

public double getPointAt(int index)

Gets an evaluation points at the specified index of the array.

Specified by:

getPointAt in interface DistributionApproximation

Parameters:

index - array index of evaluation point.

Returns:

an evaluation point.

getDensityAt

public double getDensityAt(int index)

Gets a density value at the specified index of the array.

Specified by:

getDensityAt in interface DistributionApproximation

Parameters:

index - array index of density value.

Returns:

a density value.

getMinimum

public double getMinimum()

Specified by:

getMinimum in interface DistributionApproximation

getMaximum

public double getMaximum()

Specified by:

getMaximum in interface DistributionApproximation

getMean

public double getMean()

Specified by:

getMean in interface DistributionApproximation

getVariance

public double getVariance()

getStandardDeviation

public double getStandardDeviation()

Specified by:

getStandardDeviation in interface DistributionApproximation

standardize

public double[] standardize(boolean keepPoints)

Uses current quadrature points and weights to compute the mean and standard deviation of the density, and then standardizes the distribution to have a mean of zero and a standard deviation of one. It achieves standardization in one of two possible ways. If keepPoints is true, the original points are retained and linear interpolation of the empiricial cumulative distribution is used to obtain the new weights. That is, the points are never changed, but the distribution is standardized. If keepPoints is false, the original weights are retained, but the points are transformed to standardize the distribution.

Specified by:

standardize in interface DistributionApproximation

Parameters:

keepPoints - if true original points are retained and weights are computed at these points using linear interpolation of the empirical cumulative distribution. If false, original weights are retained and standardization is achieved by linearly transforming the original points.

Returns:

transformation coefficients as a double array with two values used for the linear transformation of the points. The first value is the slope and the second value is the intercept.

getNumberOfPoints

public int getNumberOfPoints()

Gets the number of evaluation points (and corresponding number of density values).

Specified by:

getNumberOfPoints in interface DistributionApproximation

Returns:

number of evaluation points.