Dear WizaRds,
I would like to fit a curve to ten points with nls() for one
unknown parameter gamma in the Kahnemann/ Tversky function, but somehow
it won't work and I am unable to locate my mistake.
p.kum <- seq(0.1,1, by=0.1)
felt.prob.kum <- c(0.16, 0.23, 0.36, 0.49, 0.61, 0.71, 0.85, 0.89, 0.95,
1) ## how to find a function that fits these points nicely?
plot(p.kum, felt.prob.kum) ## looks a little like an "S"
gamma <- rep(0.5, 10)
nls.dataframe <- data.frame(p.kum,felt.prob.kum, gamma)
nls.kurve <- nls( formula = felt.prob.kum ~
p.kum^gamma/(p.kum^gamma+(1-p.kum)^gamma)^(1/gamma), data=nls.dataframe,
start=c(gamma=gamma), algorithm="plinear" )
summary(nls.kurve)
gives: Error in La.chol2inv(x, size) : 'size' cannot exceed nrow(x) = 10
If I go with the Gauss-Newton algorithm I get an singular gradient
matrix error, so I tried the Golub-Pereyra algorithm for partially
linear least-squares.
It also seems the nls model tries to find ten different gammas, but
I want only one single gamma parameter for the function. I appreciate
your help and support. Thank you.
sol lucet omnibus
Mark Hempelmann
Mark,
The parameter of your model (gamma) should not be a part of the dataframe.
In addition, the start argument should be a named list.
Something like this works
nls.dataframe <- data.frame(p.kum,felt.prob.kum)
nls.kurve <- nls( formula = felt.prob.kum ~
p.kum^gamma/(p.kum^gamma+(1-p.kum)^gamma)^(1/gamma), data=nls.dataframe,
start=list(gamma=.5), trace=TRUE) # trace shows convergence of the
algorithm.
but the fit is not very good as the fitted gamma is essentially 1.
Hope this helps,
Andy
__________________________________
Andy Jaworski
518-1-01
Process Laboratory
3M Corporate Research Laboratory
-----
E-mail: apjaworski at mmm.com
Tel: (651) 733-6092
Fax: (651) 736-3122
Mark Hempelmann
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Subject
[R] nls() fit to Kahnemann/ Tversky
10/31/2005 04:14 function
PM
Dear WizaRds,
I would like to fit a curve to ten points with nls() for one
unknown parameter gamma in the Kahnemann/ Tversky function, but somehow
it won't work and I am unable to locate my mistake.
p.kum <- seq(0.1,1, by=0.1)
felt.prob.kum <- c(0.16, 0.23, 0.36, 0.49, 0.61, 0.71, 0.85, 0.89, 0.95,
1) ## how to find a function that fits these points nicely?
plot(p.kum, felt.prob.kum) ## looks a little like an "S"
gamma <- rep(0.5, 10)
nls.dataframe <- data.frame(p.kum,felt.prob.kum, gamma)
nls.kurve <- nls( formula = felt.prob.kum ~
p.kum^gamma/(p.kum^gamma+(1-p.kum)^gamma)^(1/gamma), data=nls.dataframe,
start=c(gamma=gamma), algorithm="plinear" )
summary(nls.kurve)
gives: Error in La.chol2inv(x, size) : 'size' cannot exceed nrow(x) = 10
If I go with the Gauss-Newton algorithm I get an singular gradient
matrix error, so I tried the Golub-Pereyra algorithm for partially
linear least-squares.
It also seems the nls model tries to find ten different gammas, but
I want only one single gamma parameter for the function. I appreciate
your help and support. Thank you.
sol lucet omnibus
Mark Hempelmann
______________________________________________
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https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide!
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Note that a simple logistic with a saturation level of 1 seems to do quite well. Below we have removed the last point in order to avoid the singularity: x <- p.kum[-10] y <- felt.prob.kum[-10] plot(log(y/(1-y)) ~ x) abline(lm(log(y/(1-y)) ~ x), col = "red") On 10/31/05, Mark Hempelmann <neo27 at t-online.de> wrote:> Dear WizaRds, > > I would like to fit a curve to ten points with nls() for one > unknown parameter gamma in the Kahnemann/ Tversky function, but somehow > it won't work and I am unable to locate my mistake. > > p.kum <- seq(0.1,1, by=0.1) > felt.prob.kum <- c(0.16, 0.23, 0.36, 0.49, 0.61, 0.71, 0.85, 0.89, 0.95, > 1) ## how to find a function that fits these points nicely? > plot(p.kum, felt.prob.kum) ## looks a little like an "S" > > gamma <- rep(0.5, 10) > nls.dataframe <- data.frame(p.kum,felt.prob.kum, gamma) > > nls.kurve <- nls( formula = felt.prob.kum ~ > p.kum^gamma/(p.kum^gamma+(1-p.kum)^gamma)^(1/gamma), data=nls.dataframe, > start=c(gamma=gamma), algorithm="plinear" ) > > summary(nls.kurve) > > gives: Error in La.chol2inv(x, size) : 'size' cannot exceed nrow(x) = 10 > > If I go with the Gauss-Newton algorithm I get an singular gradient > matrix error, so I tried the Golub-Pereyra algorithm for partially > linear least-squares. > > It also seems the nls model tries to find ten different gammas, but > I want only one single gamma parameter for the function. I appreciate > your help and support. Thank you. > > sol lucet omnibus > Mark Hempelmann > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html >