You want the offset function in the formula: lm( A ~ B + I(B^2) + offset(C), data=Dataset)
This will force the coefficient on C to be 1, if you wanted a coefficient of another value then just do the multiplication yourself, e.g. offset( 2 * C ) for a slope of 2. Also you can use poly(B,2) to fit a linear and quadratic terms on B. On Thu, Oct 17, 2013 at 3:45 AM, Robert U <tacsun...@yahoo.fr> wrote: > Dear all, > > I have been trying to find a simple solution to my problem without > success, though i have a feeling a simple syntaxe detail coul make the job. > > I am doing a polynomial linear regression with 2 independent variables > such as : > > lm(A ~ B + I(B^2) + I(lB^3) + C, data=Dataset)) > > R return me a coefficient per independent variable, and I would need the > coefficient of the C parameter to equal 1. > > > I've been loonking at "parameter constraints" on the internet but it's > always much more complicated that just "removing" the fit of a coefficient > (or setting it to 1). > > > I know many package allows to "not fit" an intercept with a "-1" parameter > in the syntaxe, does that exists for independent variables ? > > Regards, > [[alternative HTML version deleted]] > > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > -- Gregory (Greg) L. Snow Ph.D. 538...@gmail.com [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
