com.itemanalysis.psychometrics.histogram

Class Histogram

java.lang.Object

com.itemanalysis.psychometrics.histogram.Histogram

All Implemented Interfaces:

DistributionApproximation

public class Histogram
extends java.lang.Object
implements DistributionApproximation

A histogram involves an array of value for the x-axis (i.e. the points) and an array of values for the y-axis (i.e. the values). The type of y-axis values returned the evaluate methods are determined by the argument histogramType. This class provides two different ways to create a histogram. One way involves passing an entire double array to evaluate(double[]) a second way requires that you first incrementally update the data with calls to increment(double) or setData(double[]) and then call evaluate(). If you call the method evaluate() without first calling setData(double[]) or incrementally updating the data, you will get a null pointer exception.

Author:

J. Patrick Meyer

Field Summary

Fields

Modifier and Type	Field and Description
protected boolean	interpolatorUpdateNeeded

Constructor Summary

Constructors

Constructor and Description
Histogram(HistogramType histogramType)
Histogram(HistogramType histogramType, BinCalculationType binCalculationType, boolean lowerInclusive) This constructor is the most general.
Histogram(HistogramType histogramType, int numberOfBins, double min, double max, boolean lowerInclusive) This constructor is for use with a SimpleBinCalculation where the user provides the min, max, and number of points.

Method Summary

Modifier and Type	Method and Description
double[]	evaluate() Gets an array of value values.
double[]	evaluate(double[] x)
Bin	getBinAt(int index) Histogram bins are stored in an array list.
double	getBinWidth()
double	getDensityAt(int index) Gets a value value at the specified index.
double	getMaximum()
double	getMean()
double	getMinimum()
int	getNumberOfBins() Gets the number of histogram bins.
int	getNumberOfPoints() Gets the number of evaluation points (and corresponding number of value values).
double	getPointAt(int index) Gets an evaluation points at the specified index.
double[]	getPoints() Gets an array of evaluation points.
double	getSampleSize()
double	getStandardDeviation()
double	getSumOfValues()
double	getValueAt(int index)
void	increment(double value) Incrementally counts the number of observations in each bin.
void	increment(double value, double frequency)
java.util.Iterator<Bin>	iterator() Gets the iterator for the array list of bin objects.
void	setData(double[] x)
void	setDensityAt(int index, double value)
void	setPointAt(int index, double value)
double[]	standardize(boolean keepPoints) Uses current quadrature points and weights to compute the mean and standard deviation of the density, and tehn standardizes the distribution to have a mean of zero and a standard deviation of one.
java.lang.String	toString() A string representation of the histogram.

Methods inherited from class java.lang.Object

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

Field Detail

interpolatorUpdateNeeded

protected boolean interpolatorUpdateNeeded

Constructor Detail

Histogram

public Histogram(HistogramType histogramType)

Histogram

public Histogram(HistogramType histogramType,
                 BinCalculationType binCalculationType,
                 boolean lowerInclusive)

This constructor is the most general. It can only use three of the four bin calculation classes. That is, the binCalculationType must be a FreedmanDiaconisBinCalculation, ScottBinCalculation, or SturgesBinCalculation. A SimpleBinCalculation is not permitted.

Parameters:

histogramType - indicates the type of y-axis values that will be returned when evaluate() is called. It has no other effect.

lowerInclusive - true if the bins should be lower inclusive. If false, the bins will be upper inclusive.

Histogram

public Histogram(HistogramType histogramType,
                 int numberOfBins,
                 double min,
                 double max,
                 boolean lowerInclusive)

This constructor is for use with a SimpleBinCalculation where the user provides the min, max, and number of points.

Parameters:

histogramType - type of histogram

numberOfBins - number of bins to include in histogram

min - minimum value

max - maximum value

lowerInclusive - true if lower bound is included in the interval but teh upper bound is not. If false lower bound not included but upper bound is included.

Method Detail

getSumOfValues

public double getSumOfValues()

getValueAt

public double getValueAt(int index)

increment

public void increment(double value)

Incrementally counts the number of observations in each bin. The value is counted only in the bin to which it belongs.

Parameters:

value - a value to be counted in a bin.

increment

public void increment(double value,
                      double frequency)

getNumberOfBins

public int getNumberOfBins()

Gets the number of histogram bins.

Returns:

number of bins.

getSampleSize

public double getSampleSize()

getBinAt

public Bin getBinAt(int index)

Histogram bins are stored in an array list. Gets the bin at the specified index.

Parameters:

index - position in the array list of the bin.

Returns:

a histogram bin.

getPoints

public double[] getPoints()

Gets an array of evaluation points. This method is required by the DistributionApproximation interface. The evaluation points are the bin midpoints.

Specified by:

getPoints in interface DistributionApproximation

Returns:

an array of evaluation points

setData

public void setData(double[] x)

evaluate

public double[] evaluate()

Gets an array of value values. This method is required by the DistributionApproximation interface.

Specified by:

evaluate in interface DistributionApproximation

Returns:

an array of value values.

evaluate

public double[] evaluate(double[] x)

getPointAt

public double getPointAt(int index)

Gets an evaluation points at the specified index. This method is required by the DistributionApproximation interface. The evaluation points are the bin midpoints.

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 value value at the specified index. This method is required by the DistributionApproximation interface.

Specified by:

getDensityAt in interface DistributionApproximation

Parameters:

index - array index of value value.

Returns:

value value.

setDensityAt

public void setDensityAt(int index,
                         double value)

Specified by:

setDensityAt in interface DistributionApproximation

setPointAt

public void setPointAt(int index,
                       double value)

Specified by:

setPointAt in interface DistributionApproximation

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

getStandardDeviation

public double getStandardDeviation()

Specified by:

getStandardDeviation in interface DistributionApproximation

getBinWidth

public double getBinWidth()

getNumberOfPoints

public int getNumberOfPoints()

Gets the number of evaluation points (and corresponding number of value values). This method is required by the DistributionApproximation interface.

Specified by:

getNumberOfPoints in interface DistributionApproximation

Returns:

number of evaluation points.

iterator

public java.util.Iterator<Bin> iterator()

Gets the iterator for the array list of bin objects.

Returns:

an iterator.

standardize

public double[] standardize(boolean keepPoints)

Uses current quadrature points and weights to compute the mean and standard deviation of the density, and tehn 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.

toString

public java.lang.String toString()

A string representation of the histogram. It lists the bin intervals, midpoints, and value values.

Overrides:

toString in class java.lang.Object

Returns:

histogram values for display as plain text.