What do you want to minimize? Can you write a function to compute eps given x, y, and a? Given that, you can then write another function to compute the objective function you want to minimize. If "a" is a scalar, compute the objective function for a range of values of "a" and plot. If you want numerical precision, read the help file for "optim", work the examples until you understand enough to see how to feed your objective function with a starting value to "optim".

If you still can't figure it out, please make an attempt, then read the posting guide "http://www.R-project.org/posting-guide.html";, and prepare a follow-up question as needed. (In a discussion on and off this list earlier this week, several people confirmed that they had solved many problems following this posting guide. It may not be as good as Polya's famous "How to Solve It", but it's pretty good.)

hope this helps. spencer graves

Angelo Secchi wrote:

You are right. eps in my model is not a parameter but the error term.
Also the linearization doesn't solve the problem, since sometimes you
cannot take logs. Any other ideas?
Thanks


On Fri, 18 Mar 2005 11:21:12 -0500 "Liaw, Andy" <[EMAIL PROTECTED]> wrote:



That's treating eps as a parameter in the model. If I read your question
right, that's not what you want.


Andy



From: ronggui [mailto:[EMAIL PROTECTED]

then is the nls function can deal the problem as Guillaume STORCHI mentioned in the last post? [X<-nls(y~x+exp(a*x)*eps, data=,start=list(a=,eps=))]
or just can solve the problem as:log(y-x) = a*x + e?




On Fri, 18 Mar 2005 08:56:38 -0500
"Liaw, Andy" <[EMAIL PROTECTED]> wrote:



AFAIK most model fitting techniques will only deal with

additive errors, not


multiplicative ones.  You might want to try fitting:

log(y-x) = a*x + e

which is linear.

Andy



From: Angelo Secchi

Hi,
is there a way  in R to fit a non linear model like

y=x+exp(a*x)*eps

where a is the parameter and eps is the error term? Thanks
Angelo


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