jhplot.stat

Class LinReg

java.lang.Object

jhplot.stat.LinReg

public class LinReg
extends Object

A linear regression analysis.

Constructor Summary

Constructors

Constructor and Description
LinReg(double[] aX, double[] aY) Constructor for regression calculations
LinReg(P1D aXY) constructor for regression calculations.

Method Summary

Modifier and Type	Method and Description
void	addPoint(double xValue, double yValue) Add a point to the data and redo the regression
void	doc() Show online documentation.
P1D[]	getConfidence() Get confidence intervals for means
P1D[]	getConfidence(Color color) Get confidence intervals for means
P1D	getConfidenceBand(Color color, double transparency) Calculate confidence band in form of P1D with errors.
P1D	getConfidenceBand(int Npoints, Color color, double transparency) Calculate confidence band in form of P1D with errors.
double	getCorrelation() Get correlation coefficient
int	getDataLength() Get the size of the input data
double[]	getDataX() Get an array with X data
double[]	getDataY() Get an array with Y data
double	getIntercept() Get Intercept
double	getInterceptError() Get the standard error on intercept
double	getMaxAbsoluteResidual() Get max absolute residual
double	getMaxX() Get a maximum value for X
double	getMaxY() Get maximum value in Y
double	getMinX() Get a minimum value for X
double	getMinY() Get minimum value for Y
double	getMSE() Get MSE value
double	getPearsonR() Get pearson R
P1D[]	getPrediction() Get prediction lines
P1D[]	getPrediction(Color color) Get prediction lines
P1D	getPredictionBand() Calculate the prediction band in form of P1D with errors.
P1D	getPredictionBand(Color color) Calculate the prediction band in form of P1D with errors.
P1D	getPredictionBand(Color color, double transparency) Calculate the prediction band in form of P1D with errors.
P1D	getPredictionBand(int Npoints, Color color, double transparency) Calculate the prediction band in form of P1D with errors.
P1D	getResiduals() Get residuals
F1D	getResult() Get the linear regression result
double	getSlope() Get slope
double	getSlopeError() Get the standard error on slope
double	getSSE() Get SSE value
double	getSSR() Ger SSR value
double	getSumXSquared() Get sun of the square
double	getSxx() Get Sxx value: sumXsquared - sumX * sumX / n
double	getSyy() Get SYY value: sumYsquared - sumY * sumY / n
double	getXBar() Get average x
double	getYBar() Get average Y
void	reset() reset data to 0

Methods inherited from class java.lang.Object

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

Constructor Detail

LinReg

public LinReg(double[] aX,
              double[] aY)

Constructor for regression calculations

Parameters:

aX - is the array of x data

aY - is the array of y data

LinReg

public LinReg(P1D aXY)

constructor for regression calculations. It should be noted that only X and Y values from the input P1D data holder are used

Parameters:

aXY - P1D container for X and Y values

Method Detail

reset

public void reset()

reset data to 0

getCorrelation

public double getCorrelation()

Get correlation coefficient

Returns:

Correlation coefficient.

getIntercept

public double getIntercept()

Get Intercept

Returns:

Intercept

getInterceptError

public double getInterceptError()

Get the standard error on intercept

Returns:

standard error on intercept

getSlopeError

public double getSlopeError()

Get the standard error on slope

Returns:

standard error on slope

getSlope

public double getSlope()

Get slope

Returns:

slope

getResiduals

public P1D getResiduals()

Get residuals

Returns:

P1D array with residuals

getDataX

public double[] getDataX()

Get an array with X data

Returns:

array with X data

getDataY

public double[] getDataY()

Get an array with Y data

Returns:

array with Y data

addPoint

public void addPoint(double xValue,
                     double yValue)

Add a point to the data and redo the regression

Parameters:

xValue - X value

yValue - Y value

getMinX

public double getMinX()

Get a minimum value for X

Returns:

Minimum value

getMaxX

public double getMaxX()

Get a maximum value for X

Returns:

Max value in X

getMinY

public double getMinY()

Get minimum value for Y

Returns:

minimum Y value

getMaxY

public double getMaxY()

Get maximum value in Y

Returns:

Maximum value in Y

getMaxAbsoluteResidual

public double getMaxAbsoluteResidual()

Get max absolute residual

Returns:

max absolute residual

getSxx

public double getSxx()

Get Sxx value: sumXsquared - sumX * sumX / n

Returns:

Sxx

getSyy

public double getSyy()

Get SYY value: sumYsquared - sumY * sumY / n

Returns:

Syy valye

getSSR

public double getSSR()

Ger SSR value

Returns:

SSR value

getSSE

public double getSSE()

Get SSE value

Returns:

SSE value

getMSE

public double getMSE()

Get MSE value

Returns:

MSE value

getXBar

public double getXBar()

Get average x

Returns:

average X

getYBar

public double getYBar()

Get average Y

Returns:

average Y

getDataLength

public int getDataLength()

Get the size of the input data

Returns:

size of data array

getPearsonR

public double getPearsonR()

Get pearson R

Returns:

pearson R

getSumXSquared

public double getSumXSquared()

Get sun of the square

Returns:

sum of the square

getResult

public F1D getResult()

Get the linear regression result

Returns:

Resulting function

getConfidence

public P1D[] getConfidence()

Get confidence intervals for means

Returns:

P1D[2] for lower and high

getConfidence

public P1D[] getConfidence(Color color)

Get confidence intervals for means

Parameters:

color - color used to draw

Returns:

P1D[2] for lower and high

getPrediction

public P1D[] getPrediction()

Get prediction lines

Returns:

P1D[2] for lower and high

getPrediction

public P1D[] getPrediction(Color color)

Get prediction lines

Parameters:

color - color used to draw

Returns:

P1D[2] for lower and high

getConfidenceBand

public P1D getConfidenceBand(Color color,
                             double transparency)

Calculate confidence band in form of P1D with errors. The number of total points for P1D is set to 100 by default.

Parameters:

color - Color used to show the band

transparancy - level of color transparency (between 0 and 1)

Returns:

P1D with fit values. Errors show the confidence band

getConfidenceBand

public P1D getConfidenceBand(int Npoints,
                             Color color,
                             double transparency)

Calculate confidence band in form of P1D with errors.

Parameters:

Npoints - number of points to display the band

color - Color used to show the band

transparancy - level of color transparency (between 0 and 1)

Returns:

P1D with fit values. Errors show the confidence band

getPredictionBand

public P1D getPredictionBand()

Calculate the prediction band in form of P1D with errors. The number of total points for P1D is set to 100 by default.

Returns:

P1D with fit values. Errors show the prediction band

getPredictionBand

public P1D getPredictionBand(Color color)

Calculate the prediction band in form of P1D with errors. The number of total points for P1D is set to 100 by default.

Parameters:

color - Color used to show the band

Returns:

P1D with fit values. Errors show the prediction band

getPredictionBand

public P1D getPredictionBand(Color color,
                             double transparency)

Calculate the prediction band in form of P1D with errors. The number of total points for P1D is set to 100 by default.

Parameters:

color - Color used to show the band

transparancy - level of color transparency (between 0 and 1)

Returns:

P1D with fit values. Errors show the prediction band

getPredictionBand

public P1D getPredictionBand(int Npoints,
                             Color color,
                             double transparency)

Calculate the prediction band in form of P1D with errors.

Parameters:

Npoints - number of points for evaluation

color - Color used to show the band

transparancy - level of color transparency (between 0 and 1)

Returns:

P1D with fit values. Errors show the prediction band

doc

public void doc()

Show online documentation.