papaya
Class Linear

java.lang.Object
  papaya.Linear

All Implemented Interfaces:

PapayaConstants

public class Linear
extends Object
implements PapayaConstants

Contains methods related to determining the linear linear relationship between two datasets (of equal arrays) such as the best-fit linear line parameters, box-cox transformations, etc.

Nested Class Summary

static class	Linear.BoxCox Contains methods related to the Box-Cox transformation of a data set; useful in determining the best transformation that will yield the best method for converting a monotonic, non-linear relationship between x and y into a linear one.
static class	Linear.Significance Contains methods used to compute the significance, or pvalue of the input correlations.
static class	Linear.StdErr Contains methods related to computing the standard errors of the residuals, slope and intercept associated with the best-fit linear line.

Field Summary

Fields inherited from interface papaya.PapayaConstants

BASELINE, big, biginv, BOTTOM, CENTER, CORNER, FONTNAME, GRAY, INDEX_NOT_FOUND, INDICES_NOT_FOUND, LEFT, LOGPI, MACHEP, MAXGAM, MAXLOG, MINLOG, RIGHT, SQRTH, SQTPI, STROKEWEIGHT, TEXTSIZE, TOP

Constructor Summary

Linear()

Method Summary

static float[]	bestFit(float[] x, float[] y) Returns the slope and y-intercept of the best fit linear line z = slope*x + intercept by minimizing the sum of least squares between z and the y.
static float	bestFit(float[] x, float[] y, float intercept) Returns the slope of the best fit linear line for the prescribed y-intercept.
static float[]	residuals(float[] x, float[] y) Compute and return the array of residuals given by Delta_i = z_i - y_i, where z_i = (slope*x_i + intercept) is the best fit linear line.
static float[]	residuals(float[] x, float[] y, float slope, float intercept) Compute and return the array of residuals given by Delta_i = z_i - y_i, where z_i = (slope*x_i + intercept) is the best fit linear line.

Methods inherited from class java.lang.Object

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

Constructor Detail

Linear

public Linear()

Method Detail

bestFit

public static float[] bestFit(float[] x,
                              float[] y)

Returns the slope and y-intercept of the best fit linear line z = slope*x + intercept by minimizing the sum of least squares between z and the y.

Parameters:

x - the x data

y - the y data

Returns:

a 2-by-1 array with the first element coeff[0]corresponding to the slope, and the second element coeff[1] equal to the y-intercept.

bestFit

public static float bestFit(float[] x,
                            float[] y,
                            float intercept)

Returns the slope of the best fit linear line for the prescribed y-intercept. That is, z = slope*x + intercept, where the intercept is specified by the user. E.g. intercept = 1 will result in z = slope*x+1. The slope is computed by minimizing the sum of least squares between z and the y.

Parameters:

x - the x data

y - the y data

intercept - the intercept of the best-fit line with the y-axis.

Returns:

the slope of the best-fit linear line.

residuals

public static float[] residuals(float[] x,
                                float[] y,
                                float slope,
                                float intercept)

Compute and return the array of residuals given by Delta_i = z_i - y_i, where z_i = (slope*x_i + intercept) is the best fit linear line.

Parameters:

slope - the slope of the best-fit linear line

intercept - the y-intercept of the best fit linear line

residuals

public static float[] residuals(float[] x,
                                float[] y)

Compute and return the array of residuals given by Delta_i = z_i - y_i, where z_i = (slope*x_i + intercept) is the best fit linear line. You'd basically use this to compute the spread of a best fit line (max - min)