Global Index (short | long) | Local contents | Local Index (short | long)
[fval, dof1, dof2] = vectsig(c1, c2);
[fval, dof1, dof2] = vectsig(c1, c2); This function returns the F-score (see Tatsuoka, Multivariate Analysis, 1988, pp 85 - 88). These can be compared using the tables in the back of the above mentioned book. Notice that c1 and c2 are complex matrices formed like: c1 = u1 + j*v1 where j = sqrt(-1) c1 and c2 need not have the same number of time realizations (columns), but need to have the same number of spatial real- izations (rows) Note that dof1 refers to the degrees of freedom in the denominator, while dof2 refers to the degrees of freedom in the numerator, when obtaining the F-value.
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function [fval, dof1, dof2] = vectsig(c1, c2); [m1, n1] = size(c1); [m2, n2] = size(c2); if n1 ~= n2; error('ERROR - number of stations (rows) must be the same'); end; n = n1; dif_mean = mean2(c1) - mean2(c2); c11 = c1 - ones(m1,1) * mean2(c1); c22 = c2 - ones(m2,1) * mean2(c2); for i = 1:n; s1 = [real(c11(:,i)) imag(c11(:,i))]' * [real(c11(:,i)) imag(c11(:,i))]; s2 = [real(c22(:,i)) imag(c22(:,i))]' * [real(c22(:,i)) imag(c22(:,i))]; % w = s1 + s2; % % Calculate the T statistic, ref. Tatsuoka, p. 86 % t(i) = [real(dif_mean(i)) imag(dif_mean(i))] * ... inv(w * ((m1 + m2) / (m1 * m2 * (m1 + m2 - 2)))) * ... [real(dif_mean(i)) imag(dif_mean(i))]'; end % % Calculate the F statistic, ref. Tatsuoka, p. 86 % fval = ((m1 + m2 - 3) / (2 * (m1 + m2 - 2))) * t; dof1 = m1 + m2 -3; dof2 = 2;