Click for printer friendely version of this HowTo Univariate General Linear Models function beta=general_linear(x,y,c) # y is a column (nx1) vector of observations # x is the design matrix (nxm) # c is the contrast matrix # NOTE: it is assumed that c is set up so that theta = 0 n = max(size(x)); # number of observations p = min(size(x)); # number of parameters num_tests = min(size(c)); # number of tests beta = inv(x'*x)*x'*y # estimate the parameters # test model numerator = beta'*c'*inv((c*inv(x'*x)*c'))*c*beta / num_tests; denominator = (y'*y - y'*x*beta)/(n-p); # this is also known as MSE f_test = numerator / denominator p_value = 1 - f_cdf(f_test, num_tests, n-p) # calculate p-value if (num_tests == 1) t_test = sqrt(f_test) end # generate graphical output MIN_F_DIST = 0; MAX_F_DIST = 20; NUM_STEPS = 100; graph_x = linspace(MIN_F_DIST, MAX_F_DIST, NUM_STEPS); graph_y = f_pdf(graph_x, num_tests, n-p); description = sprintf("g;F(%d, %d)-Dist;", num_tests, n-p); plot(graph_x, graph_y, description, f_test, 0, "r*;Your Data;") endfunction

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