On 2011-06-30 06:14, Niklaus Hurlimann wrote:
Greetings,
I am struggling a bit with a non-linear regression. The problem is
described below with the known values r and D inidcated.
I tried to alter the start values but get always following error
message:
Error in nlsModel(formula, mf, start,
I think that these aren't good initial values. If you do a plot of data and
add a curve, the curve don't approximate the data. Frequently I use
interactive procedures to get good initial values. Using playwith() you can
handle sliders to adjust values and use in nls(), look the following
r -
Greetings,
I am struggling a bit with a non-linear regression. The problem is
described below with the known values r and D inidcated.
I tried to alter the start values but get always following error
message:
Error in nlsModel(formula, mf, start, wts):
singular gradient matrix at initial
If you have a perfect fit, you have zero residuals. But in the nls manual page
we have:
Warning:
*Do not use ‘nls’ on artificial zero-residual data.*
So this is a case of complaining that your diesel car is broken because you ignored the
Diesel fuel only sign on the filler cap and put
Dear JN, Bert,
1) It is not a perfect fit. I do not think I have ever said that. I said
that an external algorithms fits the model without any problems: with ~
500,000 data points and 19 paramters (ki in the original equation), it
fits the model in less than 1 second. The data are not
-project.org
Subject: Re: [R] Error singular gradient matrix at initial parameter
estimates in nls
Dear JN, Bert,
1) It is not a perfect fit. I do not think I have ever said that. I said
that an external algorithms fits the model without any problems: with ~
500,000 data points and 19 paramters
-project.org
Subject: Re: [R] Error singular gradient matrix at initial parameter
estimates in nls
Dear JN, Bert,
1) It is not a perfect fit. I do not think I have ever said that. I said
that an external algorithms fits the model without any problems: with ~
500,000 data points and 19 paramters (ki
I am using nls to fit a non linear function to some data.
The non linear function is:
y= 1- exp(-(k0+k1*p1+ + kn*pn))
I have chosen algorithm port, with lower boundary is 0 for all of the
ki parameters, and I have tried many start values for the parameters ki
(including generating them
You could try method=brute-force in the nls2 package to find starting values.
On Tue, Mar 30, 2010 at 7:03 AM, Corrado ct...@york.ac.uk wrote:
I am using nls to fit a non linear function to some data.
The non linear function is:
y= 1- exp(-(k0+k1*p1+ + kn*pn))
I have chosen algorithm
Hi Gabor,
same problem even using nls2 with method=brute-force to calculate the
initial parameters.
Best,
Gabor Grothendieck wrote:
You could try method=brute-force in the nls2 package to find starting values.
On Tue, Mar 30, 2010 at 7:03 AM, Corrado ct...@york.ac.uk wrote:
I am using
Sorry, its algorithm=brute-force
On Tue, Mar 30, 2010 at 10:29 AM, Corrado ct...@york.ac.uk wrote:
Hi Gabor,
same problem even using nls2 with method=brute-force to calculate the
initial parameters.
Best,
Gabor Grothendieck wrote:
You could try method=brute-force in the nls2 package to
Yes, of course. The problem still stays.
Gabor Grothendieck wrote:
Sorry, its algorithm=brute-force
On Tue, Mar 30, 2010 at 10:29 AM, Corrado ct...@york.ac.uk wrote:
Hi Gabor,
same problem even using nls2 with method=brute-force to calculate the
initial parameters.
Best,
Gabor
What do you mean the problem still stays? If you are using brute
force its not a problem to have it fail on some of the evaluations
since each one is separate. How large a grid are you using? Are you
claiming that every single point on the grid fails? Please provide
reproducible code showing
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