If I understand this correctly the variables over which you are optimizing are p[1], p[2] and p[3] whereas x and y are fixed and known during the optimization. In that case its a linear programming problem and you could use the lpSolve library which would allow the explicit specification of the constraints.
On 11/28/05, Florent Bresson <[EMAIL PROTECTED]> wrote: > I have to estimate the following model for several > group of observations : > > y(1-y) = p[1]*(x^2-y) + p[2]*y*(x-1) + p[3]*(x-y) > > with constraints : > p[1]+p[3] >= 1 > p[1]+p[2]+p[3]+1 >= 0 > p[3] >= 0 > > I use the following code : > func <- sum((y(1-y) - p[1]*(x^2-y) + p[2]*y*(x-1) + > p[3]*(x-y))^2) > estim <- optim( c(1,0,0),func, method="L-BFGS-B" , > lower=c(1-p[3], -p[1]-p[3]-1, 0) ) > > and for some group of observations, I observe that the > estimated parameters don't respect the constraints, > espacially the first. Where's the problem please ? > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
