[R] Error 'singular gradient' in nonlinear model fitting

[Douglas Bates]

On Feb 15, 2008 11:50 AM, Gabor Grothendieck <ggrothendieck at gmail.com> wrote:
> Try fitting regressing log(WEIGHT) against log(TOTAL) using lm
> and then transform the results into starting values for nls (or
> possibly that is sufficient and you don't need the nls results).

Also, set the trace argument to TRUE in your call to nls.  This will
cause nls to print the progress of the iterations including the
residual sum of squares and the parameter values.  What usually
happens with a singular gradient is that one of the parameters,
probably beta, is heading to an extreme value.

This particular model can be reduced to a one-parameter optimization
problem by using the optional "plinear" algorithm.

nls(WEIGHT ~ (TOTAL^beta)/454, start = c(beta = 3), data =
spottedseatrout2004.female.data, algorithm = "plinear")

> On Fri, Feb 15, 2008 at 12:41 PM, HongSheng Liao <HLiao at odu.edu> wrote:
> >
> > w.age.female.2004 <- nls(WEIGHT ~ (alpha*TOTAL^beta)/454,
> >                        start=list(alpha=1, beta=3),
> >                        data=spottedseatrout2004.female.data)
> >
> > I am trying to fit above model to length-weight data of a fish species
> > (spotted seatrout) by year (1999-2006).  The convergence occurred for all
> > the years except 2002 and 2004.  In these two year, R shows the error
> > called 'singular gradient' in the model.  Could anyone please help me to
> > fix the error?  I don't think there are any problems with my data because I
> > can fit the same model to 2004 data without any problems using SAS.  Thank
> > you very much in advance.
> >
> > Hongsheng (Hank) Liao, Ph.D.
> > Lab Manager
> > Center for Quantitative Fisheries Ecology
> > 800 West 46th Street
> > Old Dominion University
> > Norfolk, Virginia 23508
> > Phone:757.683.4571
> > Fax:757.683.5293
> >
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> > and provide commented, minimal, self-contained, reproducible code.
> >
>
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> and provide commented, minimal, self-contained, reproducible code.
>