My way of handling this kind of problem is to assume that x and / or y follow some probability distribution with parameters a and b. Then I write a function to compute deviance = (-2*log(likelihood)) = (-2*log(probability density for x and / or y given a and b)) and feed it to "optim" to minimize this "deviance". Or if the response variable is a nonlinear model plus independent normal errors with constant variance, I may use "nls".
hope this helps. spencer graves
Sophie Gerber wrote:
Dear Colleagues,
I have x, y data (pollen and seed dispersal from oaks !) that I would like to fit with a function which look like this:
p(a,b,x,y)=b/(2*pi*a�gamma(2/b))*exp(-(square_root(x�+y�)/a)power(b))
I am looking for a and b values that fit my data at best. Can someone give me hints to perform such an analysis with R ?
Thanks a lot
Sophie
Sophie Gerber [EMAIL PROTECTED] INRA - UMR BIOGECO 69 route d'Arcachon tel (33) (0)5 57 12 28 30 33612 Cestas cedex fax (33) (0)5 57 12 28 81 http://www.pierroton.inra.fr/genetics/Perso/Sophie/ page_sophie_english.html
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