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 withadditive 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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