[R] nonlinear curve fit of an implicit function
Ravi Varadhan
rvaradhan at jhmi.edu
Tue Oct 5 17:44:58 CEST 2010
You can solve this as a (nonlinearly) constrained optimization problem:
Given: x, y.obs; both in R^n
Minimize the objective function: sum( (y - y.obs)^2 )
where `y' is such that it satisfies the constraints: (A+B+C)*log((B+C+y)/C)-A*log((B-y)/B)=(B+C)*D*x
Do you have other constraints on A,B, and C, such as positivity, etc? If so, you need to incorporate them as well.
You can take a look at the package "alabama", which has 2 optimizers: `auglag' and `constrOptim.nl'.
Hope this helps,
Ravi.
____________________________________________________________________
Ravi Varadhan, Ph.D.
Assistant Professor,
Division of Geriatric Medicine and Gerontology
School of Medicine
Johns Hopkins University
Ph. (410) 502-2619
email: rvaradhan at jhmi.edu
----- Original Message -----
From: "Schmitt, H. (Heike)" <H.Schmitt at uu.nl>
Date: Tuesday, October 5, 2010 11:10 am
Subject: [R] nonlinear curve fit of an implicit function
To: r-help at r-project.org
> Hello,
>
> I want to perform a nonlinear curve fit in order to obtain parameter
> estimates from experimentally determined data (y in dependence of x),
> but with an implicit function, thus, a function of which I cannot
> isolate y on the left-hand side of the equation. As far as I understand,
> the functions I found up to now (nls, optim) all work only for explicit
> functions.
>
> My data looks like
> x<-c(4, 7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46, 49, 52,
> 55, 58, 61)
> y<-c(0.0, -0.1, 0.0, 0.1, 0.5, 0.9, 1.4, 2.3, 3.1, 3.4, 3.8, 4.2, 4.6,
> 4.9, 5.0, 5.1, 5.2, 5.3, 5.4, 5.5)
>
>
>
> My function is a sigmoidal growth function based on an integrated form
> of the Monod function:
>
> (A+B+C)*log((B+C+y)/C)-A*log((B-y)/B)=(B+C)*D*x
>
>
>
> Your ideas would be greatly appreciated.
>
>
>
> Heike Schmitt
>
> Utrecht University
>
>
>
>
> [[alternative HTML version deleted]]
>
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> and provide commented, minimal, self-contained, reproducible code.
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