[R] problem with nlsLM.....

[akshay kulkarni]

dear Ivan,
                   THanks for the reply......
But doesn't removing some of the parameters reduce the precision of the relationship between the response variable and the predictors(inefficient estimates of the coefficients)?

very many thanks for your time and effort....
yours sincerely,
AKSHAY M KULKARNI
________________________________
From: Ivan Krylov <krylov.r00t using gmail.com>
Sent: Wednesday, March 20, 2019 3:06 PM
To: akshay kulkarni
Cc: R help Mailing list
Subject: Re: [R] problem with nlsLM.....

On Wed, 20 Mar 2019 08:02:45 +0000
akshay kulkarni <akshay_e4 using hotmail.com> wrote:

> formulaDH5 <- as.formula(HM1 ~ (a + (b * ((HM2 + 0.3)^(1/2)))) +
> (A*sin(w*HM3 + c) + C))

The problem with this formula is simple: the partial derivative with
respect to `a` is the same as the partial derivative with respect to
`C`. This makes the regression problem have an infinite number of
solutions, all of them satisfying equation \lambda_1 * a + \lambda_2 *
C + \lambda_3 = 0 for some values of \lambda_i. Gradient-based
optimizers (which both nls and nlsLM are) don't like problems with
non-unique solutions, especially when the model function has same
partial derivative with respect to different variables, making them
indistinguishable.

Solution: remove one of the variables.

> > formulaDH3
> HM1 ~ (a + (b * ((HM2 + 0.3)^(1/3)))) * (c * log(HM3 + 27))

The problem with this formula is similar, albeit slightly different.

Suppose that (a, b, c) is a solution. Then (\lambda * a, \lambda * b,
c / \lambda) is also a solution for any real \lambda. Once again,
removing `c` should get rid of ambiguity.

> I've checked the Internet  for a method of getting the starting
> values, but they are not comprehensive....any resources for how to
> find the starting values?

Starting values depend on the particular function you are trying to
fit. The usual approach seems to be in transforming the formula and
getting rid of parts you can safely assume to be small until it looks
like linear regression, or applying domain specific knowledge (e.g.
when trying to it a peak function, look for the biggest local maximum
in the dataset).

If you cannot do that, there also are global optimization algorithms
(see `nloptr`), though they still perform better on some problems
and worse on others. It certainly helps to have upper and lower
bounds on all parameter values. I've heard about a scientific group
creating a pool of many initial Levenberg-Marquardt parameter estimates,
then improving them using a genetic algorithm. The whole thing
"converged overnight" on a powerful desktop computer.

--
Best regards,
Ivan

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