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 p2 p4
199.48276 16.28664 -0.01987
residual sum-of-squares: 560.6
Number of iterations to convergence: 5
Achieved convergence tolerance: 5.637e-07
Ciao!
mario
On 12-Apr-11 18:01, Felix Nensa wrote:
fit = nls(yeps ~ p1 / (1 + exp(p2 - x)) * exp(p4 * x))
--
Ing. Mario Valle
Data Analysis and Visualization Group | http://www.cscs.ch/~mvalle
Swiss National Supercomputing Centre (CSCS) | Tel: +41 (91) 610.82.60
v. Cantonale Galleria 2, 6928 Manno, Switzerland | Fax: +41 (91) 610.82.82
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