R help - Apr 2011 - Non linear Regression: "singular gradient matrix at initial parameter estimates"

Hi,

I am using nls to fit a non linear function to some data but R keeps giving
me "singular gradient matrix at initial parameter estimates" errors.
For testing purposes I am doing this:

### R code ###

x <- 0:140
y <- 200 / (1 + exp(17 - x)/2) * exp(-0.02*x) # creating 'perfect'
samples
with fitting model
yeps <- y + rnorm(length(y), sd = 2) # adding noise

# results in above error
fit = nls(yeps ~ p1 / (1 + exp(p2 - x) / p3) * exp(p4 * x))

###
>From what I've found in this list I think that my model is
over-parameterized.
How can I work around that?
Thanks,

Felix

	[[alternative HTML version deleted]]

On 2011-04-11 13:29, Felix Nensa wrote:
> Hi,
>
> I am using nls to fit a non linear function to some data but R keeps giving
> me "singular gradient matrix at initial parameter estimates"
errors.
> For testing purposes I am doing this:
>
> ### R code ###
>
> x<- 0:140
> y<- 200 / (1 + exp(17 - x)/2) * exp(-0.02*x) # creating
'perfect' samples
> with fitting model
> yeps<- y + rnorm(length(y), sd = 2) # adding noise
>
> # results in above error
> fit = nls(yeps ~ p1 / (1 + exp(p2 - x) / p3) * exp(p4 * x))
>
> ###
>
>> From what I've found in this list I think that my model is
over-parameterized.
> How can I work around that?
Take out p3; it's redundant.

Peter Ehlers
> Thanks,
>
> Felix
>
> 	[[alternative HTML version deleted]]
>
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