com.itemanalysis.psychometrics.distribution

Class NormalDistributionApproximation

java.lang.Object

com.itemanalysis.psychometrics.distribution.AbstractDistributionApproximation

com.itemanalysis.psychometrics.distribution.NormalDistributionApproximation

All Implemented Interfaces:

DistributionApproximation

public final class NormalDistributionApproximation
extends AbstractDistributionApproximation

An immutable object for creating evaluation points and associated density values from a normal distribution. This class creates a set of evenly spaced evaluation points between the the minimum and maximum values. Normal density values for each point are also computed. By default the distribution is a standard normal distribution (mean=0, sd=1), but the user cna optionally provide a different mean and standard deviation.

Field Summary

Fields inherited from class com.itemanalysis.psychometrics.distribution.AbstractDistributionApproximation

max, min, normal, numberOfPoints, points, range, step, weights

Constructor Summary

Constructors

Constructor and Description
NormalDistributionApproximation(double mean, double sd, double min, double max, int numberOfPoints) Creates a numerical approximation to a normal distribution with the specified mean and standard deviation.
NormalDistributionApproximation(double min, double max, int numberOfPoints) Creates a numerical approximation to the standard normal distribution.

Method Summary

Modifier and Type	Method and Description
void	setDensityAt(int index, double value)
void	setPointAt(int index, double value)
java.lang.String	toString()

Methods inherited from class com.itemanalysis.psychometrics.distribution.AbstractDistributionApproximation

evaluate, getDensityAt, getMaximum, getMean, getMinimum, getNumberOfPoints, getPointAt, getPoints, getStandardDeviation, getVariance, setNormalPointsAndWeights, setUniformPointsAndWeights, standardize

Methods inherited from class java.lang.Object

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

Constructor Detail

NormalDistributionApproximation

public NormalDistributionApproximation(double min,
                                       double max,
                                       int numberOfPoints)
                                throws java.lang.IllegalArgumentException

Creates a numerical approximation to the standard normal distribution. Evaluation points will lie between the minimum and maximum values. The number of evaluation points is specified as an argument.

Parameters:

min - minimum evaluation point.

max - maximum evaluation point.

numberOfPoints - number of evaluation points (and corresponding number of density values).

Throws:

java.lang.IllegalArgumentException

NormalDistributionApproximation

public NormalDistributionApproximation(double mean,
                                       double sd,
                                       double min,
                                       double max,
                                       int numberOfPoints)
                                throws java.lang.IllegalArgumentException

Creates a numerical approximation to a normal distribution with the specified mean and standard deviation. Evaluation points will lie between the minimum and maximum values. The number of evaluation points is specified as an argument.

Parameters:

mean - mean of the normal distribution.

sd - standard deviation of the normal distribution.

min - minimum evaluation point.

max - maximum evaluation point.

numberOfPoints - number of evaluation points (and corresponding number of density values).

Throws:

java.lang.IllegalArgumentException

Method Detail

setDensityAt

public void setDensityAt(int index,
                         double value)

setPointAt

public void setPointAt(int index,
                       double value)

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object