MATLAB高阶累积量工具箱.rar

function [sg, sl] = glstat(x,cparm,nfft) %GLSTAT Detection statistics for Hinich's Gaussianity and linearity tests % [sg, sl] = glstat(x,cparm,nfft) % x - time series % cparm - resolution (smoothing) parameter c; % 0.5 < c < 1.0 [default = 0.51] % increasing c reduces the variance, % but increases the bias (window bandwidth). % nfft - fft length [default = 128] % sg - statistic for the Gaussianity test % [observed chi_sq value, dg, PFA] % sl - statistic for the linearity test % [R_estimated, lambda, R_theoretical] % % Under the assumption of Gaussianity, the test statistics S is % chi-squared distributed with df degrees of freedom. % Under the assumption of non-zero skewness and linearity, the test % statistic R should be approximately equal to the inter-quartile % range of a chi-squared distribution with two degrees of freedom, % and non-centrality parameter lambda. % % Copyright (c) 1991-98 by United Signals & Systems, Inc. and The MathWorks, Inc. % $Revision: 1.4 $ % A. Swami January 20, 1993. % A. Swami, October 14, 1994, Revised. % A. Swami, February 23, 1995, added `N' printout in linearity test % RESTRICTED RIGHTS LEGEND % Use, duplication, or disclosure by the Government is subject to % restrictions as set forth in subparagraph (c) (1) (ii) of the % Rights in Technical Data and Computer Software clause of DFARS % 252.227-7013. % Manufacturer: United Signals & Systems, Inc., P.O. Box 2374, % Culver City, California 90231. % % This material may be reproduced by or for the U.S. Government pursuant % to the copyright license under the clause at DFARS 252.227-7013. % ---------------- parameter checks: ---------------------- if (min(size(x)) ~= 1) error('x: should be a vector') end nsamp = length(x); if (exist('nfft') ~= 1) nfft = 128; end nrecs = floor(nsamp/nfft); nrecs = max(nrecs,1); ksamp = min(nfft,nsamp); if (nfft > nsamp) disp([' glstat results are unreliable if zero-padding is severe: ']) disp([' fft length= ',int2str(nfft),' data length=',int2str(nsamp)]) end if (exist ('cparm') ~= 1) cparm = 0.51; end if (cparm >= 1.0) error('cparm cannot be greater than or equal to 1.') end if (cparm <=0.5 & nrecs == 1) error('cparm: for single segments: allowed range is (0.5,1.0) ') end M = fix(nfft^cparm); M = M + 1 - rem(M,2); Msmuth = M; % ----------------- estimate raw bispectrum ------------------------ mrow = fix(nfft/3)+1; ncol = fix(nfft/2)+1; F = zeros(mrow,ncol); S = zeros(nfft,1); mask = hankel ([1:mrow],[mrow:mrow+ncol-1]); ind = 1:ksamp; for k=1:nrecs y = x(ind); xf = fft(y-mean(y), nfft); xc = conj(xf); S = S + xf .* xc; % power spectrum F = F + xf(1:mrow) * xf(1:ncol).' .* ... reshape (xc(mask), mrow, ncol) ; ind = ind + ksamp; end F = F /(nfft*nrecs); % `Raw' bispectrum F as in Eq (2.3) S = S /(nfft*nrecs); % `Raw' power spectrum % ---------- zero out area outside principal domain --------------- ind = (0:(mrow-1))'; F(1:mrow,1:mrow) = triu(F(1:mrow,1:mrow)); Q = ones(mrow, ncol); Q(1:mrow,1:mrow) = triu(Q(1:mrow,1:mrow)) + eye(mrow); % the 2f1 + f2 = 1 line: r = ( rem(nfft,3) == 2); % ans oct 94: also -r next line for k=mrow+1:ncol-r j = k-mrow; Q(mrow-2*j+2:mrow, k) = zeros(2*j-1,1); F(:, k) = F(:,k) .* Q(:,k); Q(mrow-2*j+1, k) = 2; % factor 2 on boundary end F = F(2:mrow,2:ncol); % in principal domain, no dc terms Q = Q(2:mrow,2:ncol); mrow = mrow-1; ncol = ncol-1; % -------- smooth the estimated spectrum and bispectrum ------------ % Msmuth x Msmuth box-car smoother % only every Msmuth term in the smoothed output is retained % "independent" estimates M = Msmuth; mby2 = (M+1)/2; m1 = rem(mrow,M); m2=rem(ncol,M); m1 = - m1 + M * (m1 ~= 0); m2 = - m2 + M * (m2 ~= 0); F = [F, zeros(mrow,m2); zeros(m1,ncol+m2)]; Q = [Q, zeros(mrow,m2); zeros(m1,ncol+m2)]; k = size(F)/Msmuth; k1 = k(1); k2 = k(2); % Apply the box car smoother % can replace the next ten lines or so with % B =kron(eye(k1),ones(1,Msmuth)) * F * kron(eye(k2),ones(Msmuth,1))/Msmuth^2; % Q =kron(eye(k1),ones(1,Msmuth)) * Q * kron(eye(k2),ones(Msmuth,1)); ind = 1:Msmuth; B = zeros(k1,k2); Q1 = B; for i=1:k1 for j=1:k2 t = F( (i-1)*Msmuth+ind, (j-1)*Msmuth+ind ); B(i,j) = mean(t(:)); t = Q( (i-1)*Msmuth+ind, (j-1)*Msmuth+ind ); Q1(i,j) = sum(t(:)); end end Q = Q1; % -------------- % At this point B corresponds to B in eq (2.6) of Hinich's paper % and Q corresponds to the definition following eq (2.8) % -------------- M = Msmuth; S = conv(S, ones(M,1))/M; S = S(M+1:M:M+nfft-1); S1 = B*0; % S1 = S(1:k1) * S(1:k2)' .* hankel(S(1:k1),S(k1:k1+k2-1)); S1 = S(1:k1) * S(1:k2)' .* hankel(S(2:k1+1),S(k1+1:k1+k2)); S1 = ones(k1,k2) ./ S1; ind = find(Q > 0); Q = Q(ind); Bic = S1(ind) .* abs(B(ind)).^2; % squared bicoherence %------------------------------------------------------------------- %----------------- Gaussianity (non-skewness) test ----------------- %------------------------------------------------------------------- scale = 2 * (Msmuth^4) / nfft; Xstat = scale * Bic ./ Q; % 2 |X(m,n)|^2 in (2.9) df = length(Q) * 2; % degrees of freedom % asymptotic value is (nfft/M)^2 / 6 chi_val = sum(Xstat); % observed value of chi-square r.v. % Reference: % Sankaran's approximation given in [7.9.4], p 197, of % J.K. Patel and C.B. Read, M. Dekker, New York, 1982. % `Handbook of the Normal Distribution' % the cdf of chi(df,lam,x) is approximated by phi(y) [normal distro cdf] % where y = [ (x/(df+lam))^h - a ] / b; % and h, a, b are defined below % For a given df, and chi-sq value, the PFA is the probability that % we will be wrong in accepting the alternate hypothesis; i.e., % data are non-skewed. h = 1/3; b = sqrt(2/df) / 3; a = (1-b^2); y = (chi_val/df)^(1/3); y = (y-a)/b; pfa = 0.5 * erfc(y/sqrt(2)); pfa = round(pfa*10000)/10000; % round to five decimal places disp(['Test statistic for Gaussianity is ', num2str(chi_val), ... ' with df = ',int2str(df), ', Pfa = ',num2str(pfa)]) sg = [chi_val, df, pfa]; % ----------------------------------------------------------------- % ---------------------- linearity test --------------------------- % ----------------------------------------------------------------- % ---- find the first and third quartiles of 2|Xmn|^2 = Xstat ind = find(Q == M^2); rtest = Xstat(ind); rtest = sort(rtest); l1 = length(rtest); if (l1 < 4) fprintf('glstat: cparm (%g) is too large or nfft (%g) is too small', ... cparm,nfft); fprintf('\n estimated interquartile range (R) set to NaN \n'); Rhat = NaN; else lo1 = fix(l1/4); lo2 = fix(l1/4+0.8); hi1 = fix(3*l1/4); hi2 = fix(3*l1/4+0.8); r1 = (rtest(lo1)+rtest(lo2))/2; r3 = (rtest(hi1)+rtest(hi2))/2; Rhat = r3 - r1; % sample inter-quartile range end % ----- estimated lambda: a scaled version of mean FD skewness % lam = 2/(df * Msmuth^2) * sum ( Q.*Xstat - 2); % Hinich's (4.2) lam = mean(Bic) * 2 * Msmuth^2 / nfft; % equivalent %----- find the `theoretical inter-quartile' range of chi-sq(df=2,lam) % See reference cited earlier. df = 2; % for our problem, df=2 h = 1/3 + (2/3) * (