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
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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