BENG 449 problem 7 3

BENG 449 problem 7 3 - 4/3/08 10:34 PM MATLAB Command...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
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);
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 07/19/2008 for the course BENG 449 taught by Professor Richardcarson during the Spring '08 term at Yale.

Page1 / 6

BENG 449 problem 7 3 - 4/3/08 10:34 PM MATLAB Command...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online