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BENG 449 problem 7 3

# BENG 449 problem 7 3 - 10:34 PM MATLAB Command Window 1 of...

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4/3/08 10:34 PM MATLAB Command Window 1 of 3 >> %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Alex Lemon % % BENG 449 -- Biomedial Data Analysis % % Problem Set 7, Question 3 % % Due April 4th, 2008 % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Perform 1000 simulations of noisy data with 21 free ligand concentrations % over the range 1 to 200, evenly spaced logarithmically. Use paramater % values: Bmax = 25, Kd = 10, sigma = 1. Fit the data with the nonlinear % model and the linear Stratchard reformulation. num_iter = 1000; n = 21; Fmin = 1; Fmax = 200; logFmin = log(Fmin); logFmax = log(Fmax); deltaLogF = (logFmax - logFmin) / (n - 1); logF = logFmin:deltaLogF:logFmax; F = exp( logF ); Bmax = 25; Kd = 10; sigma = 1; eta = BofF( [Bmax Kd], F ); NonlinearBmax = zeros(1, num_iter); NonlinearKd = zeros(1, num_iter); LinearBmax = zeros(1, num_iter); LinearKd = zeros(1, num_iter); for(i = 1:num_iter) % Generate the noisy data B = eta + sigma * randn(1,n); % Calculate the nonlinear parameter estimates beta0 = [(rand(1)+0.5)*Bmax (rand(1)+0.5)*Kd]; options = optimset('display','off','LargeScale','off'); betaN = lsqcurvefit( @BofF, beta0, F, B, [], [], options ); NonlinearBmax(i) = betaN(1); NonlinearKd(i) = betaN(2); % Calculate the Stratchard linearized parameter estimates X = [ones(n,1) B']; y = (B ./ F)';

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