Hi list,
 
I have a little curve fitting problem.
 
I would like to fit a sigmoid curve to my data using the following equation:
 
f(x) = 1/(1 + exp(-(x-c)*b)) (or any other form for that matter)
 
Where x is the distance/location within the dataframe, c is the shift of the curve across the dataframe and b is the steepness of the curve.
 
I've been playing with glm() and glm.fit() but without any luck.
 
for example the most simple example
 
x = -10:10
y = 1/(1 + exp(-x))
glm(y ~ x, family=binomial(link="logit"))
 
I get a warning:
non-integer #successes in a binomial glm! in: eval(expr, envir, enclos) 
 
and some erratic results
 
This is the most simple test to see if I could fit a curve to this perfect data so since this didn't work out, bringing in the extra parameters is a whole other ballgame so could someone give me a clue?
 
Kind regards,
Koen
 

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Checked by AVG Free Edition.

 7:52
 

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