Hi Peter,

thank you for your reply. Now I see, that P3 is indeed redundand.
But with the simplified model...

fit = nls(yeps ~ p1 / (1 + exp(p2 - x)) * exp(p4 * x))

...nls still produces the same error.
Any ideas?

Felix

2011/4/12 Peter Ehlers <ehlers@ucalgary.ca>

> 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
>>
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>>
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>>
>
>

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