Greetings,
The description of nls.lm specifies that in minimizing a sum of squares of residuals the number of residuals must be no less than the dimension of the independent variable ("par"). In fact the algorithm does not work otherwise (termination code 0). But this condition is senseless, since it can be vacuously satisfied by adding zero residuals without altering the minimization problem. Nor, to the best of my knowledge does the number of residuals play a role in the Levenberg-Marquardt algorithm. So why does the R-implementation need this condition? I am also not clear how the Jacobian should be formatted. I am assuming that it contains the gradients of the residuals in the same order as the residuals occur in the function fn -- but this is not working for me. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.