Dear All, I am using optim() for a relatively simple task: a linear model where instead of minimizing the sum of the squared errors, I minimize the sum of the squared relative errors. However, I notice that the default algorithm is very sensitive to the choice of the initial fit parameters, whereas I get much more stable (and therefore better?) results with the BFGS algorithm. I would like to have some feedback on this (perhaps I made a mistake somewhere). I provide a small self-contained example. You can download a tiny data set from the link
https://www.dropbox.com/s/tmbj3os4ev3d4y8/data-instability.csv?dl=0 whereas I paste the script I am using at the end of the email. Any feedback is really appreciated. Many thanks Lorenzo ################################################################ min.perc_error <- function(data, par) { with(data, sum(((par[1]*x1 + par[2]*x2+par[3]*x3 - y)/y)^2)) } par_ini1 <- c(.3,.1, 1e-3) par_ini2 <- c(1,1, 1) data <- read.csv("data-instability.csv") mm_def1 <-optim(par = par_ini1 , min.perc_error, data = data) mm_bfgs1 <-optim(par = par_ini1 , min.perc_error, data = data, method="BFGS") print("fit parameters with the default algorithms and the first seed ") print(mm_def1$par) print("fit parameters with the BFGS algorithms and the first seed ") print(mm_bfgs1$par) mm_def2 <-optim(par = par_ini2 , min.perc_error, data = data) mm_bfgs2 <-optim(par = par_ini2 , min.perc_error, data = data, method="BFGS") print("fit parameters with the default algorithms and the second seed ") print(mm_def2$par) print("fit parameters with the BFGS algorithms and the second seed ") print(mm_bfgs2$par) ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
