On Wed, 1 Jul 2020 14:31:19 +0200
Luigi Marongiu <marongiu.lu...@gmail.com> wrote:
> the optimization actually got a worse outcome than the original
>eyeball estimation
Could you elaborate on the function you are trying to fit to your data?
nls.lm takes a function returning a vector of residuals, that is,
fn <- function(parameters, input, actual.output) {
calculate.output(parameters, input) - actual.output
}
...which means that you need a vector of input values and a vector of
output values of the same length. In your example,
> y = (p$a * x^2) / (p$b^2 + x^2)
a and b are parameters. x seems to be the dependent variable (i.e.
output of the process) and not the independent variable (i.e. input of
the model function) like I had initially assumed. What is the input of
your model function?
--
Best regards,
Ivan
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