One way will be to solve this as an ordinary optimization problem with
an equality constraint. Function `alabama::auglag` can do this:
library(alabama)
fn <- function(p) sum((df$y - p[1]*exp(-p[2]*df$x))^2)
heq <- function(p) sum(p[1]*exp(-p[2]*df$x)) - 5
# Start with initial
> On Jun 20, 2018, at 8:50 AM, Lorenzo Isella wrote:
>
> Dear All,
> I have a problem I haver been struggling with for a while: I need to
> carry out a non-linear fit (and this is the
> easy part).
> I have a set of discrete values {x1,x2...xN} and the corresponding
> {y1, y2...yN}. The
I recommend posting this on a mathematics discussion forum like Stack Exchange
and (re-)reading the Posting Guide for this mailing list.
I think you are going to need to re-write your model function to algebraically
combine your original model along with the constraint, and then use the
Dear All,
I have a problem I haver been struggling with for a while: I need to
carry out a non-linear fit (and this is the
easy part).
I have a set of discrete values {x1,x2...xN} and the corresponding
{y1, y2...yN}. The difficulty is that I would like the linear fit to
preserve the sum of the
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