Hi Mario,
yes works great. Thanks!
2011/4/12 Mario Valle
> Use a more realistic starting point instead of the default one:
>
> fit <- nls(yeps ~ p1 / (1 + exp(p2 - x)) * exp(p4 * x),
> start=list(p1=410,p2=18,p4=-.03))
>
> This works for me:
> > fit
> Nonlinear regression model
> model: yeps ~
Use a more realistic starting point instead of the default one:
fit <- nls(yeps ~ p1 / (1 + exp(p2 - x)) * exp(p4 * x),
start=list(p1=410,p2=18,p4=-.03))
This works for me:
> fit
Nonlinear regression model
model: yeps ~ p1/(1 + exp(p2 - x)) * exp(p4 * x)
data: parent.frame()
p1
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
> On 2011-04-11 13:29, Felix Nensa wrote:
>
>> Hi,
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*
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 fit
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