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
[[alternative HTML version deleted]]
dear members,
also,I can provide HM1,HM2 and HM3 if needed....
________________________________________
From: R-help <r-help-bounces at r-project.org> on behalf of akshay
kulkarni <akshay_e4 at hotmail.com>
Sent: Tuesday, March 19, 2019 5:43 PM
To: R help Mailing list
Subject: [R] problem with nlsLM function
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
[[alternative HTML version deleted]]
______________________________________________
R-help at 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.
[[alternative HTML version deleted]]
On 19/03/2019 8:26 a.m., akshay kulkarni wrote:> > dear members, > also,I can provide HM1,HM2 and HM3 if needed.... > > ________________________________________ > From: R-help <r-help-bounces at r-project.org> on behalf of akshay kulkarni <akshay_e4 at hotmail.com> > Sent: Tuesday, March 19, 2019 5:43 PM > To: R help Mailing list > Subject: [R] problem with nlsLM function > > 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))You have "a" twice in your start list. That's bound to cause trouble... Duncan Murdoch> 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 > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at 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. > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at 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. >