# 14.7 Do Two-Dimensional Distributions Differ?

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1 14.7 Do Two-Dimensional Distributions Differ? 645 li=l/j; decoding its row lj=l-j*li; and column. mm=(m1=li-ki)*(m2=lj-kj); pairs=tab[ki+1][kj+1]*tab[li+1][lj+1]; if (mm) Not a tie. en1 += pairs; en2 += pairs; s += (mm > 0? pairs : -pairs); Concordant, or discordant. else if (m1) en1 += pairs; if (m2) en2 += pairs; *tau=s/sqrt(en1*en2); svar=(4.0*points+10.0)/(9.0*points*(points-1.0)); *z=(*tau)/sqrt(svar); *prob=erfcc(fabs(*z)/ ); CITED REFERENCES AND FURTHER READING: Lehmann, E.L. 1975, Nonparametrics: Statistical Methods Based on Ranks (San Francisco: Holden-Day). Downie, N.M., and Heath, R.W. 1965, Basic Statistical Methods, 2nd ed. (New York: Harper & Row), pp Norusis, M.J. 1982, SPSS Introductory Guide: Basic Statistics and Operations; and 1985, SPSS- X Advanced Statistics Guide (New York: McGraw-Hill) Do Two-Dimensional Distributions Differ? We here discuss a useful generalization of the K S test ( 14.3) to two-dimensional distributions. This generalization is due to Fasano and Franceschini [1], a variant on an earlier idea due to Peacock [2]. In a two-dimensional distribution, each data point is characterized by an (x, y) pair of values. An example near to our hearts is that each of the 19 neutrinos that were detected from Supernova 1987A is characterized by a time t i and by an energy E i (see [3]). We might wish to know whether these measured pairs (t i,e i), i= are consistent with a theoretical model that predicts neutrino flux as a function of both time and energy that is, a two-dimensional probability distribution in the (x, y) [here, (t, E)] plane. That would be a one-sample test. Or, given two sets of neutrino detections, from two comparable detectors, we might want to know whether they are compatible with each other, a two-sample test. In the spirit of the tried-and-true, one-dimensional K S test, we want to range over the (x, y) plane in search of some kind of maximum cumulative difference between two two-dimensional distributions. Unfortunately, cumulative probability distribution is not well-defined in more than one dimension! Peacock s insight was that a good surrogate is the integrated probability in each of four natural quadrants around a given point (x i,y i), namely the total probabilities (or fraction of data) in (x >x i,y > y i), (x <x i,y > y i), (x <x i,y < y i), (x >x i,y < y i). The two-dimensional K S statistic D is now taken to be the maximum difference (ranging both over data points and over quadrants) of the corresponding integrated probabilities. When comparing two data sets, the value of D may depend on which data set is ranged over. In that case, define an effective D as the average

2 646 Chapter 14. Statistical Description of Data Figure Two-dimensional distributions of 65 triangles and 35 squares. The two-dimensional K S test finds that point one of whose quadrants (shown by dotted lines) maximizes the difference between fraction of triangles and fraction of squares. Then, equation (14.7.1) indicates whether the difference is statistically significant, i.e., whether the triangles and squares must have different underlying distributions. of the two values obtained. If you are confused at this point about the exact definition of D, don t fret; the accompanying computer routines amount to a precise algorithmic definition. Figure gives a feeling for what is going on. The 65 triangles and 35 squares seem to have somewhat different distributions in the plane. The dotted lines are centered on the triangle that maximizes the D statistic; the maximum occurs in the upper-left quadrant. That quadrant contains only 0.12 of all the triangles, but it contains 0.56 of all the squares. The value of D is thus Is this statistically significant? Even for fixed sample sizes, it is unfortunately not rigorously true that the distribution of D in the null hypothesis is independent of the shape of the two-dimensional distribution. In this respect the two-dimensional K S test is not as natural as its one-dimensional parent. However, extensive Monte Carlo integrations have shown that the distribution of the two-dimensional D is very nearly identical for even quite different distributions, as long as they have the same coefficient of correlation r, defined in the usual way by equation (14.5.1). In their paper, Fasano and Franceschini tabulate Monte Carlo results for (what amounts to) the distribution of D as a function of (of course) D, sample size N, and coefficient of correlation r. Analyzing their results, one finds that the significance levels for the two-dimensional K S test can be summarized by the simple, though approximate, formulas, ( ) ND Probability (D >observed )=Q KS 1+ 1 r 2 ( / (14.7.1) N)

4 648 Chapter 14. Statistical Description of Data *d1=fmax(*d1,fabs(fd-gd)); For both the sample and the model, the distribution is integrated in each of four quadrants, and the maximum difference is saved. pearsn(x1,y1,n1,&r1,&dum,&dumm); Get the linear correlation coefficient r1. sqen=sqrt((double)n1); rr=sqrt(1.0-r1*r1); Estimate the probability using the K-S probability function probks. *prob=probks(*d1*sqen/(1.0+rr*( /sqen))); void quadct(float x, float y, float xx[], float yy[], unsigned long nn, float *fa, float *fb, float *fc, float *fd) Given an origin(x, y), and an array of nn points with coordinates xx[1..nn] and yy[1..nn], count how many of them are in each quadrant around the origin, and return the normalized fractions. Quadrants are labeled alphabetically, counterclockwise from the upper right. Used by ks2d1s and ks2d2s. unsigned long k,na,nb,nc,nd; float ff; na=nb=nc=nd=0; for (k=1;k<=nn;k++) if (yy[k] > y) xx[k] > x? ++na : ++nb; else xx[k] > x? ++nd : ++nc; ff=1.0/nn; *fa=ff*na; *fb=ff*nb; *fc=ff*nc; *fd=ff*nd; #include "nrutil.h" void quadvl(float x, float y, float *fa, float *fb, float *fc, float *fd) This is a sample of a user-supplied routine to be used with ks2d1s. In this case, the model distribution is uniform inside the square 1 <x<1, 1 <y<1. In general this routine should return, for any point (x, y), the fraction of the total distribution in each of the four quadrants around that point. The fractions, fa, fb, fc, and fd, must add up to 1. Quadrants are alphabetical, counterclockwise from the upper right. float qa,qb,qc,qd; qa=fmin(2.0,fmax(0.0,1.0-x)); qb=fmin(2.0,fmax(0.0,1.0-y)); qc=fmin(2.0,fmax(0.0,x+1.0)); qd=fmin(2.0,fmax(0.0,y+1.0)); *fa=0.25*qa*qb; *fb=0.25*qb*qc; *fc=0.25*qc*qd; *fd=0.25*qd*qa; The routine ks2d2s is the two-sample case of the two-dimensional K S test. It also calls quadct, pearsn, and probks. Being a two-sample test, it does not need an analytic model.

5 14.7 Do Two-Dimensional Distributions Differ? 649 #include <math.h> #include "nrutil.h" void ks2d2s(float x1[], float y1[], unsigned long n1, float x2[], float y2[], unsigned long n2, float *d, float *prob) Two-dimensional Kolmogorov-Smirnov test on two samples. Given the x and y coordinates of the first sample as n1 values in arrays x1[1..n1] and y1[1..n1], and likewise for the second sample, n2 values in arrays x2 and y2, this routine returns the two-dimensional, two-sample K-S statistic as d, and its significance level as prob. Small values of prob show that the two samples are significantly different. Note that the test is slightly distribution-dependent, so prob is only an estimate. void pearsn(float x[], float y[], unsigned long n, float *r, float *prob, float *z); float probks(float alam); void quadct(float x, float y, float xx[], float yy[], unsigned long nn, float *fa, float *fb, float *fc, float *fd); unsigned long j; float d1,d2,dum,dumm,fa,fb,fc,fd,ga,gb,gc,gd,r1,r2,rr,sqen; d1=0.0; for (j=1;j<=n1;j++) quadct(x1[j],y1[j],x1,y1,n1,&fa,&fb,&fc,&fd); quadct(x1[j],y1[j],x2,y2,n2,&ga,&gb,&gc,&gd); d1=fmax(d1,fabs(fa-ga)); d1=fmax(d1,fabs(fb-gb)); d1=fmax(d1,fabs(fc-gc)); d1=fmax(d1,fabs(fd-gd)); d2=0.0; for (j=1;j<=n2;j++) quadct(x2[j],y2[j],x1,y1,n1,&fa,&fb,&fc,&fd); quadct(x2[j],y2[j],x2,y2,n2,&ga,&gb,&gc,&gd); d2=fmax(d2,fabs(fa-ga)); d2=fmax(d2,fabs(fb-gb)); d2=fmax(d2,fabs(fc-gc)); d2=fmax(d2,fabs(fd-gd)); *d=0.5*(d1+d2); sqen=sqrt(n1*n2/(double)(n1+n2)); pearsn(x1,y1,n1,&r1,&dum,&dumm); pearsn(x2,y2,n2,&r2,&dum,&dumm); rr=sqrt( *(r1*r1+r2*r2)); First, use points in the first sample as origins. Then, use points in the second sample as origins. Average the K-S statistics. Get the linear correlation coefficient for each sample. Estimate the probability using the K-S probability function probks. *prob=probks(*d*sqen/(1.0+rr*( /sqen))); CITED REFERENCES AND FURTHER READING: Fasano, G. and Franceschini, A. 1987, Monthly Notices of the Royal Astronomical Society, vol. 225, pp [1] Peacock, J.A. 1983, Monthly Notices of the Royal Astronomical Society, vol. 202, pp [2] Spergel, D.N., Piran, T., Loeb, A., Goodman, J., and Bahcall, J.N. 1987, Science, vol. 237, pp [3]

6 650 Chapter 14. Statistical Description of Data 14.8 Savitzky-Golay Smoothing Filters In 13.5 we learned something about the construction and application of digital filters, but little guidance was given on which particular filter to use. That, of course, depends on what you want to accomplish by filtering. One obvious use for low-pass filters is to smooth noisy data. The premise of data smoothing is that one is measuring a variable that is both slowly varying and also corrupted by random noise. Then it can sometimes be useful to replace each data point by some kind of local average of surrounding data points. Since nearby points measure very nearly the same underlying value, averaging can reduce the level of noise without (much) biasing the value obtained. We must comment editorially that the smoothing of data lies in a murky area, beyond the fringe of some better posed, and therefore more highly recommended, techniques that are discussed elsewhere in this book. If you are fitting data to a parametric model, for example (see Chapter 15), it is almost always better to use raw data than to use data that has been pre-processed by a smoothing procedure. Another alternative to blind smoothing is so-called optimal or Wiener filtering, as discussed in 13.3 and more generally in Data smoothing is probably most justified when it is used simply as a graphical technique, to guide the eye through a forest of data points all with large error bars; or as a means of making initial rough estimates of simple parameters from a graph. In this section we discuss a particular type of low-pass filter, well-adapted for data smoothing, and termed variously Savitzky-Golay [1], least-squares [2], ordispo (Digital Smoothing Polynomial) [3] filters. Rather than having their properties defined in the Fourier domain, and then translated to the time domain, Savitzky-Golay filters derive directly from a particular formulation of the data smoothing problem in the time domain, as we will now see. Savitzky-Golay filters were initially (and are still often) used to render visible the relative widths and heights of spectral lines in noisy spectrometric data. Recall that a digital filter is applied to a series of equally spaced data values f i f(t i), where t i t 0 + i for some constant sample spacing and i =... 2, 1, 0, 1, 2,... We have seen ( 13.5) that the simplest type of digital filter (the nonrecursive or finite impulse response filter) replaces each data value f i by a linear combination g i of itself and some number of nearby neighbors, n R g i = c nf i+n (14.8.1) n= n L Here n L is the number of points used to the left of a data point i, i.e., earlier than it, while n R is the number used to the right, i.e., later. A so-called causal filter would have n R =0. As a starting point for understanding Savitzky-Golay filters, consider the simplest possible averaging procedure: For some fixed n L = n R, compute each g i as the average of the data points from f i nl to f i+nr. This is sometimes called moving window averaging and corresponds to equation (14.8.1) with constant c n =1/(n L + n R +1). If the underlying function is constant, or is changing linearly with time (increasing or decreasing), then no bias is introduced into the result. Higher points at one end of the averaging interval are on the average balanced by lower points at the other end. A bias is introduced, however, if the underlying function has a nonzero second derivative. At a local maximum, for example, moving window averaging always reduces the function value. In the spectrometric application, a narrow spectral line has its height reduced and its width increased. Since these parameters are themselves of physical interest, the bias introduced is distinctly undesirable. Note, however, that moving window averaging does preserve the area under a spectral line, which is its zeroth moment, and also (if the window is symmetric with n L = n R) its mean position in time, which is its first moment. What is violated is the second moment, equivalent to the line width. The idea of Savitzky-Golay filtering is to find filter coefficients c n that preserve higher moments. Equivalently, the idea is to approximate the underlying function within the moving window not by a constant (whose estimate is the average), but by a polynomial of higher order, typically quadratic or quartic: For each point f i, we least-squares fit a polynomial to all

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