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 <-
c(1.
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, wt
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 parame
: r-help@r-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 poi
AM
Cc: r-help@r-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 ~
5
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 artifici
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
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 wh
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 wrote:
Hi Gabor,
same problem even using nls2 with method=brute-force to calculate the
initial parameters.
Best,
Gabor Grothendieck wrote:
Sorry, its algorithm="brute-force"
On Tue, Mar 30, 2010 at 10:29 AM, Corrado 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 f
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 wrote:
I am using nls to fit a no
You could try method="brute-force" in the nls2 package to find starting values.
On Tue, Mar 30, 2010 at 7:03 AM, Corrado 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 "port",
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
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