problem with nls....

On Fri, 22 Mar 2019 12:29:14 +0000

How do I get the gradient, Hessian, and the jacobian of the
objective function created by call to the nls?

nls() return value is a list containing an entry named `m`, which is an
object of type "nlsModel". It doesn't seem to be documented in modern
versions of R[*], so what I am describing might be an implementation
detail subject to change. Still, model$m$gradient() should return the
jacobian; Hessian is usually estimated as crossprod() of jacobian; and
the gradient of the objective function is computed as
-2*colSums(model$m$resid() * model$m$gradient()).

Also, I've checked the residuals...they are approximately normally
distributed....I am still wondering why the nls call is not getting
converged....!

The more important question is, how does the objective function (sum of
squared residuals) depend on the parameter `m` you are trying to find?
Try computing it for various values of `m` and looking at the result:

plot(
	Vectorize(
		function(m) {
			model$m$setPars(m);
			model$m$deviance()
		}
	),
	from = ..., to = ... # fill as needed
)

Best regards,
Ivan

[*] But used to be:
http://unixlab.stat.ubc.ca/R/library/stats/html/nlsModel.html