dear members,
I am getting the "singular gradient error" when I use nls
for a function of two variables:
> formulaDH5
HM1 ~ (a + (b * ((HM2 + 0.3)^(1/2)))) + (A * sin(w * HM3 + a) +
C)
HM1 is the response variable, and HM2 and HM3 are predictors.
The problem is I get the same error even when I use nlsLM(in the minpack.lm
package):
> nonlin_modDH5 <- nlsLM(formulaDH5, start = list(a = 0.43143, b = 2,A = 0.09,w
> = 0.8,a = 0.01,C = 0.94))
Error in nlsModel(formula, mf, start, wts) :
singular gradient matrix at initial parameter estimates
> nonlin_modDH5 <- nlsLM(formulaDH5, start = list(a = 1, b = 2,A = 0.09,w =
> 0.8,a = 0.01,C = 0.94))
Error in nlsModel(formula, mf, start, wts) :
singular gradient matrix at initial parameter estimates
> nonlin_modDH5 <- nlsLM(formulaDH5, start = list(a = 1, b = 2,A = 0.09,w =
> 0.8,a = 0.01,C = 2))
Error in nlsModel(formula, mf, start, wts) :
singular gradient matrix at initial parameter estimates
I came to know that nlsLM converges when nls throws a singular gradient error.
What is happening above? Can the problem get solved if I use nls.lm
function(in the minpack.lm package) instead?
very many thanks for your time and effort....
yours sincerely,
AKSHAY M KULKARNI
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