Re: [R] Singular Gradient in nls

n is done on the weighted gradient matrix; if the >>estimate of the rank that results is less than the number of columns in >>the gradient (the number of nonlinear parameters), or less than the number >>of rows (the number of observations), nls stops. >> >>You can see t

[R] Singular Gradient in nls

//Referring to the response posted many years ago, copied below, what is the specific criterium used for singularity of the gradient matrix? Is a Singular Value Decomposition used to determine the singular values? Is it the gradient matrix condition number or some other criterion for determ

[R] [Re: Significance of confidence intervals in the Non-Linear Least Squares Program.]

What should I be looking for in the output of the nls() routine that alerts me to the fact that the Hessian is potentially ill-conditioned? Glenn Peter Dalgaard wrote: > glenn andrews wrote: > >> Thanks for the response. I was not very clear in my original request. >> >>

[R] [Re: Significance of confidence intervals in the Non-Linear Least Squares Program.]

c 0.337570.13480 2.504 0.0189 * d -2.941652.25287 -1.306 0.2031 Glenn Prof Brian Ripley wrote: > On Wed, 26 Mar 2008, glenn andrews wrote: > >> I am using the non-linear least squares routine in "R" -- nls. I have a >> dataset where the nls r

[R] Significance of confidence intervals in the Non-Linear Least Squares Program.

I am using the non-linear least squares routine in "R" -- nls. I have a dataset where the nls routine outputs tight confidence intervals on the 2 parameters I am solving for. As a check on my results, I used the Python SciPy leastsq module on the same data set and it yields the same answer as